High-temperature-resistant fiber Bragg gratings (FBGs) are the main competitors to thermocouples as sensors in applications for high temperature environments defined as being in the 600–1200 °C temperature range. Due to their small size, capacity to be multiplexed into high density distributed sensor arrays and survivability in extreme ambient temperatures, they could provide the essential sensing support that is needed in high temperature processes. While capable of providing reliable sensing information in the short term, their long-term functionality is affected by the drift of the characteristic Bragg wavelength or resonance that is used to derive the temperature. A number of physical processes have been proposed as the cause of the high temperature wavelength drift but there is yet no credible description of this process. In this paper we review the literature related to the long-term wavelength drift of FBGs at high temperature and provide our recent results of more than 4000 h of high temperature testing in the 900–1000 °C range. We identify the major components of the high temperature wavelength drift and we propose mechanisms that could be causing them. The majority of present-day industrial processes require complex monitoring and control. Over the last decade, the demand for sensors having high accuracy, repeatability, reliability and the capability to be easily distributed has increased significantly. While there is a great variety of sensors which exploit different principles and generally fulfill this demand, many sensors will simply fail or will not perform according to the requirements when used in high temperature processes and extreme environments, such as in the presence of intensive ionizing radiation, strong electromagnetic field, corrosive ambient etc.Traditionally, the majority of temperature measurements for applications in the 600–1200 °C temperature range were done with various thermocouples ,4,5 and (1)Regenerated fiber Bragg gratings (RFBG) made by using ultraviolet (UV) lasers and the phase mask technique ,(2)FBGs made by using femtosecond lasers and the phase mask technique (Type II FBGs) ,13.(3)FBGs made by using femtosecond lasers and the point-by-point (PbP) technique (PbP FBGs) .A relatively large assortment of FOSs, ranging from simple fiber devices that exploit Rayleigh , BrillouAll of the above types of FBGs  have good thermal stability up to 1000 °C, whereas the classical FBGs  are almost completely erased at 700 °C. RFBGs were discovered in 1997  as a resThe inscription in silica fibers of high temperature resistant FBGs using infrared (IR) ultrafast radiation and the phase mask technique was demonstrated for the first time in 2003 . The appBλ. The fulfillment of the Bragg condition leads to a simple relation between Bλ and the parameters defining the FBG structure, namely the effective refractive index of the core effn and the period of the refractive index modulation ΛG:The idea underlying the use of FBGs as temperature sensors is straightforward. A FBG temperature sensor is a narrow-band interference filter whose reflection spectrum is centered at a characteristic wavelength which is called the Bragg wavelength effn and ΛG are functions of temperature, Bλ is also correlated with the ambient temperature. Type II FBGs, for example, demonstrated near-linear wavelength drifts with temperature of 16.8 pm/ °C in the 800–1000 °C temperature range [As both re range .Using this basic principle, high temperature resistant FBGs were deployed in a considerable number of applications. RFBGs were used to monitor the temperature of a melt during metal processing , the temType II FBG arrays have been used in several applications: for monitoring temperature gradients of the flame tube of a low emission burner , to moniA number of tests were also performed in nuclear installations. As an example, RFBGs were placed in liquid sodium at 500 °C for high temperature monitoring of a sodium-cooled fast nuclear reactor . Four fiThe above published results confirm that FBG sensors can potentially replace thermocouples in some applications and, moreover, improve the quality of temperature measurements by leveraging the unique advantages of FOSs. However, at temperatures approaching 1000 °C, the silica glass of which the fiber is made of, starts changing its properties, which could potentially lead to degradation in the sensor’s performance. The first experiments to establish the survivability of FBG structures at high temperature mainly concentrated on monitoring the reflectivity of the FBGs under test . The nexeffn and ΛG due to several effects such as dopant diffusion from the core, devitrification  of the glass, and release of stresses that are built into the fiber during the fiber manufacturing process. Hence, a more specific aim of this study is to look at the most probable mechanisms of the Bragg wavelength drift in silica fibers and suggest ways to mitigate their effect. This becomes especially important because the initial proof-of-concept successes with 1000 °C-resistant sapphire FBGs [Studying the Bragg wavelength drift of high temperature resistant FBGs  at a constant temperature  is the main subject of this paper. It summarizes previously reported experiments and presents our recent tests performed with Type II FBGs. If no stress is applied to the fiber, the Bragg wavelength drift at a constant temperature (see Equation (1)) will be caused by changes in ire FBGs ,34,35 anire FBGs  were notBλ) is a function of the FBG’s period (ΛG) and the modal effective refractive index (effn). The latter is determined both by the fiber and the refractive index modulation inside the FBG, which in turn depends on the internal structure of the FBG. Therefore, one of the first questions to ask is whether the Bragg wavelength drift at high temperature is caused by transformations occurring in the optical fiber itself or in the FBG internal structure, or both. In order to answer this question the FBG internal structure should be known. Below we list the available information on the FBG internal structure.According to Equation (1), the Bragg wavelength of an FBG  having a core of a much smaller diameter that confines the light, preventing it to leak to the external region called cladding. The manufacturing process of optical fiber consist of drawing the fiber from a preform heated in a furnace to the glass softening temperature. When the fiber exits the furnace it starts cooling from high (above 1700 °C) temperature to room temperature after which it is coated and wound on a drum. Besides the intended effect of creating the core/cladding refractive index structure, the drawing and cooling processes result in a pattern of stresses  frozen into the fiber after the drawing process is finished . The froIn this section, we present a summary of experiments performed to study high temperature wavelength drift of: (1) RFBGs, (2) Type II FBGs and (3) PbP FBGs. Experiments on high temperature annealing of other structures such as (i) ablation FBGs in photonic crystal fibers (PCF), (ii) FBGs in chiral fibers, (iii) Fabry-Pérot fiber cavities and (iv) nanogratings in bulk glasses are also included in the discussion as they provide information about possible mechanisms, such as dopant diffusion or stress relaxation, that may cause a wavelength drift of FBGs. The long-term wavelength stability of RFBGs at high temperature was presented for the first time in 2005 . During During the test, the Bragg wavelength experienced a red drift of ~480 pm during the first 125 h of annealing and then a 190 pm blue drift  during the remaining 270 h. The initial red drift of 480 pm was likely the result of the regeneration process. The majority of the red drift took place in the first 25 h at a rate of ~16 pm/h. Looking for an explanation for the post-regeneration drift at 1000 °C, the authors examined samples of the annealed fiber for signs of devitrification using an optical microscope but did not find any crystalline regions in the fiber.A much longer annealing experiment at 1000 °C was performed on RFBGs in 2011 . The aut2-loaded SMF-28-type fiber  were annealed for 9000 h at different temperatures between 700 °C and 900 °C. The annealing temperature of each RFBG was continuously monitored by an individual thermocouple giving 760 °C, 810 °C, 850 °C and 890 °C. As can be seen in The longest high temperature test performed with RFBGs was reported in  when fouSome correlation between the evolutions of the Bragg wavelengths and the reflectivities of the RFBGs at 760 °C and 810 °C is also observable. The behavior of the RFBGs at higher temperatures was more erratic. For example, the Bragg wavelength of the RFBG at 890 °C experienced a red drift of ~500 pm in the first 500 h, then a blue drift of ~1200 pm in the next 3000 h, remained stable for further ~3000 h and finally experienced a red drift of ~700 pm. It is also to be noted that variations in the behavior the RFBGs became stronger and faster as the annealing temperature increased.In 2018, a simultaneous annealing in a tube furnace at 800 °C of a PbP FBG made in SMF-28 and a RFBG also made in SMF-28 was performed  see Fig. The 800Results of a 2200-h annealing test performed at 920–950 °C with four PbP FBGs were published in 2018 . Four PbThe earliest report of the wavelength drift at 1000 °C of Type II FBGs made with infrared ultrafast radiation and a phase mask was presented in . SMF-28 2-loaded SMF-28 using the phase mask technique and 800 nm, 120 fs pulses. In this time frame, gratings fabricated in pristine SMF-28 experienced a ~300 pm red drift while the gratings made in H2-loaded SMF-28 exhibited a much more modest red drift of ~100 pm  test  at 1000 0 pm see .A very simple wavelength drift experiment  was desiλ = 800 nm was used to inscribe π-shifted Type II FBGs in SMF-28 using a π-shifted phase mask with a period of 3.21 μm. To test how the FBGs responded to high temperature annealing, a number of devices were temperature-cycled many times inside a tube furnace from 20 °C to 1000 °C in ambient air. During this testing  cavity made of a 60 µm section of SMF-28 with its end faces coated with a low reflectivity Cr-layer . The optIn order to directly analyze the magnitude of dopant diffusion, the radial distribution of the Ge-dopant after the annealing process was measured using Energy Dispersive X-ray analysis (SEM/EDX) on polished end surfaces of the fiber samples subjected to different temperatures. Another parameter that can affect the wavelength drift of FBGs at high temperature is the chemical composition of the host fiber. Commercially available silicate fibers include a pure silica core fiber that usually has a fluoride-doped cladding, silica fiber with low and high germanium concentration in the fiber core, fiber whose core is co-doped with boron and active fiber. There are no published comprehensive studies that would correlate the stability of the Type II FBGs with the chemical composition of the fibers, but individual studies have shown that pure silica core fibers and fibers with low germanium concentration are more stable at high temperature than fibers with high germanium concentration. A thorough study is very difficult and expensive to conduct as it would require customized fabrication of numerous samples of special fiber.2-SiO2 binary glasses and the final study focused on the effect of other dopants like TiO2- and B2O3. According to the results presented in An alternative approach was used in  where th2. There are only small differences between the four glasses, which the authors attributed to the fact that GeO2 and SiO2 form a relatively continuous mixture whose viscosity and, therefore, thermal stability are not changed much until a certain concentration of GeO2 is exceeded. Doping silicate glasses with other oxides (TiO2 or (B2O3) significantly affects the high temperature thermal behavior  using a cylindrical lens (CL) with a focal distance of 15 mm (see 1 = 1.07 µm (first-order mask) and Λ3 = 3.21 µm (third-order mask) were used to fabricate the FBG samples. Depending on the distance between the optical fiber (F) and the phase mask (M), the interference pattern at the fiber could be produced by the beams from mainly +1/−1 diffraction orders or the beams originating from all of the available diffraction orders, i.e., 0 and +1/−1 diffraction orders in the case of a first-order mask and 0, +1/−1, +2/−2 and +3/−3 diffraction orders in the case of a third order mask [The output Gaussian beam ~7 mm in diameter at the 1/5 mm see . A 7 mm-der mask . An examDuring inscription the π-shifted Type II FBGs were continuously monitored with a tunable laser system and the laser exposure was stopped when their strength reached approximately −15 dB in transmission. The prepared FBG samples were then spliced to fiber pigtails terminated with APC connectors and placed into a Lindberg Blue M Tube Furnace  for annealing. Three identical Lindberg furnaces were used, each accommodating 4–5 samples. An external Type K thermocouple  was placed in each furnace, in close proximity of the furnace’s built-in thermocouple, and monitored using data acquisition software. FBGs were centered within the furnaces, in-between the two thermocouples , loosely resting in a hollow fused silica semi-cylinder so that they were free from strain and not touching the furnace side walls. Using FBG optical interrogators, each FBG was monitored independently using two separate interrogator channels, with one channel monitoring reflection and the other monitoring the light transmitted through the FBG and an optical isolator.Full spectrum optical data was generally collected every two hours, with the Bragg peak wavelengths being logged every 5 s in order to facilitate data processing and provide additional resolution in time. Two interrogators were used: a Hyperion SI255 FBG Interrogator  with 16 Channels and 8 pm wavelength resolution and a Micron Optics SM125 FBG Interrogator with four Channels and 5 pm wavelength resolution.In a separate experiment, three π-phase shifted gratings, prepared under identical conditions to the ones above, were then packaged in a protective quartz tubing. Their long-term wavelength stability was studied in 0–1000 °C temperature range with 0.1 pm wavelength resolution using an 8164 Lightwave Measurement System  with an integrated tunable laser and detector modules. For the packaged fibers, only the wavelength drifts measured at 1000 °C in the horizontal tube furnace are reported in this paper, while the bulk of the results will be reported elsewhere. The temperature of the annealing furnace was controlled with an external S-type thermocouple. The tips of the packaged fibers with inscribed FBGs were placed in a massive Inconel block (to decrease temperature fluctuations below 0.1 °C) and placed in the middle of the furnace where the temperature profile of the furnace has a maximum. The drift rate at 1000 °C in pm/h was calculated by dividing the Bragg wavelength changes at 600 °C, which were measured in the caesium pressure-controlled heat pipe  before a(1)Type-a gratings made with 80 fs pulses (Fourier-transform-limited pulses) and a first-order phase mask;(2)Type-b gratings made with 400 fs pulses (chirped pulses) and a third-order phase mask;(3)Type-c gratings made with 80 fs pulses (Fourier-transform-limited pulses) and a third-order phase mask.Five independent annealing tests were run at 900 °C and 1000 °C. In each test, a set of either 4 or 5 Type II FBGs fabricated under the same experimental conditions and with similar spectral characteristics was annealed for a time duration of up to 1000 h. The purpose of the tests was to gather information regarding the influence of the laser writing conditions, annealing temperature and annealing history on the drift of the wavelength of the π-phase shift passband feature. The wavelength of the passband feature in the FBG spectrum will be called the ‘Bragg wavelength’ to avoid confusion. Three different types of gratings were tested, each of them being a Type II FBG made with different exposure condition and resulting in a different grating structure as presented above in Type-a, Type-b and Type-c FBGs were inscribed using a 0.7 mJ, 0.96 mJ and 1.03 mJ pulse energy, respectively. This choice of pulse energy was made in order to equalise the growth rate of the different types of FBGs. Type-a, Type-b and Type-c FBGs did not show any statistical difference between their spectra. A typical spectral shape of the pi-phase shifted FBGs fabricated for the present experiment is presented in Figure 7b of , with thThe wavelength of the π-phase shifted passband feature in the FBG spectrum was recorded every two seconds to measure the spectral drift, while the FBG spectra were recorded every two hours to evaluate the transmission loss as a function of time.Four FBGs of Type-a were placed in the furnace at 1000 °C and left at this temperature for 660 h. The FBGs were continouslly monitored both in transmission and reflection. Variations in grating reflectivity are shown in As can be seen in In the second part of this test, the temperature was decreased from 1000 °C to 900 °C in ~1 h and left at this temperature for ~500 h. For the entire second part of this test, the reflectivity of the FBGs remained constant, a proof that the refractive index modulation remained unchanged. There was however wavelength drift in the first 150 h and, as before, especially in the first 50 h (drift rate from 0.4 pm/h to 1 pm/h). For the remainder of the test, the FBGs were remarkably stable (less than 0.1pm/h drift rate), with only one FBG (green trace) showing a slight blue drift .The behavior of the five Type-b FBGs in this set is quite different from that of the Type-a FBGs. First, the grating structure of Type-b FBGs is much more stable than that of Type-a FBGs. Indeed, the reflectivity of Type b FBGs decreased only by a couple percent during annealing at 1000 °C for ~700 h a. SecondFive Type-c FBGs were tested at 1000 °C for ~500 h see a. There Following the annealing protocols for Type-a and Type-b FBGs, the temperature was decreased to 900 °C b. After As can be seen in For this experiment, a set of 5 Type-b FBGs was tested directly at 900 °C without pre-annealing at 1000 °C in order to compare the annealing behavior at this temperature with that of the Type-b FBGs that were first pre-annealed at 1000 °C for hundreds of hours and then monitored at 900 °C. The drift toward longer wavelengths  at the bThree Type-c FBGs were packaged in quartz tubing and the drift rates were measured in the 0–1000 °C temperature region to assess the influence of packaging on FBG performance. Only the drift rates obtained at 1000 °C in the horizontal tube furnace are shown in this paper in As explained in detail in The wavelength drift pattern presented as a general feature in the Literature Review section also holds true for the above results at 900 °C, 1000 °C, or at 900 °C after annealing the FBGs for hundreds of hours at 1000 °C. This pattern is made of a fast wavelength drift at the beginning of the annealing process and a slow blue secondary drift. The red drift rate for annealing at 900 °C and 1000 °C is 1–2 pm/h and 5–20 pm/h, respectively. The blue drift rate is 0.05–0.1 pm/h and 0.2–0.5pm/h for the annealing at 900 °C and 1000 °C, respectively. It is also characteristic to the wavelength drift pattern that in between the periods of red drift and blue drift there is a period of almost no drift for 50–300 h.While this is a general conclusion reached by analyzing the drift patterns, there is also a certain degree of variability among the results. In each set of Type-a and Type-c FBGs we tested, there was an outlier in terms of drift rate. This indicates that in these cases, and to a certain extent in the case of Type-b FBGs, there is variability in the internal structure of individual FBGs. This variability is most likely caused by the different exposure conditions of the fiber core to the femtosecond radiation. Type-a FBGs were written at a small fiber-to-mask distance (~50 μm) where the beams from 0 and +1/−1 diffraction orders strongly contribute to the resultant Talbot-like interference pattern . The samBeside the variability of drift rates within individual FBG sets, there is also a group variability among the three types of FBGs tested. While Type-b FBGs are quite different in their behavior compared to Type-a and Type-c FBGs, there are only small differences in the behavior of Type-a and Type-c FBGs, which is unexpected as the periods of FBGs made with first- and third order phase masks are obviously different.The most important results of the annealing tests are (i) the difference in the red drift rate for Type-b FBGs that were annealed directly at 900 °C 2 pm/h;  and thosBased on these observations, there is no straightforward answer to the question if the wavelength drift pattern is caused by temperature-induced changes in the refractive index of the optical fiber or re-organization of the grating structure. Nonetheless, the commonality of the red and blue drift patterns for widely different grating structures seems to indicate that the wavelength drift is primarily determined by the high temperature behavior of the optical fiber itself.The experimental results above, from previous reports and our new experimental studies presented in this paper, show that the accuracy of long-term temperature monitoring using FBGs will be strongly affected by isothermal Bragg wavelength drift if measurements are performed at high temperatures, i.e., at 800–1000 °C. This can be seen by converting the accumulated drift of the Bragg wavelength  into a fictitious short-term temperature variation  that would cause a Bragg wavelength drift of the same magnitude. More specifically, to compete with standard high temperature thermocouples in terms of accuracy, the wavelength drift of a FBG should not exceed a few tens of picometers if one assumes that a 16.8 pm change in the Bragg wavelength corresponds to a ~1 °C temperature variation in the 800 °C to 1000 °C temperature range . So far,As mentioned above, the experimental data presented in the literature, fall into two distinct categories: the wavelength drift patterns of FBGs during 12 to 9000 h of annealing at 900 °C to 1000 °C and the results of high temperature tests specially designed to study processes that may affect the wavelength drift, such as dopant diffusion, stress and glass chemical composition.The diverse results on the Bragg wavelength behavior at 1000 °C have nonetheless a certain degree of commonality. The most evident common feature of all the tests presented here is a very fast red drift (2–50 pm/h rate) in the beginning of 900 °C or 1000 °C annealing tests followed by a slower blue drift that can last for hundreds of hours. The red wavelength drift can also last for 100–200 h but the majority of the wavelength drift takes place in the first 10–30 h after the 1000 °C temperature has been reached. This trend is observed in ,48 at 10What is the cause of the rapid red drift? As it was mentioned earlier in the Introduction, there are several processes that can cause isothermal wavelength drift at high temperature. First, it is dopant diffusion, a well-known effect used in industry to enlarge the core of the fiber in a process called Thermally Expanded Core (TEC) . While TSecond, it is the fiber manufacturing process. During the drawing of the optical fiber and the fast cooling of the fiber, the internal stresses are frozen into the fiber (frozen-in stress). The residual stress induced during fiber drawing can be very large and thus cause changes in the refractive index of the optical fiber through the photoelastic effect ,74. AccoIt is possible that frozen–in stresses are released during annealing at ~1000 °C, causing an increase in the fiber refractive index and, as a consequence, a red drift of the Bragg wavelength at the beginning of the 1000 °C annealing process. High temperature pre-annealing at temperatures in excess of 1000 °C, such as 1100 °C or 1200 °C could release frozen-in stresses and thus mitigate the red drift during the first stage of annealing at a lower temperature ,60. ThisThe second stage of the annealing process at 1000 °C consists of a slow blue drift ranging between 0.1 and 0.5 pm/h that lasts for hundreds of hours. Based on the definition of the Bragg wavelength, Equation (1), the blue wavelength drift should result from either a decrease in the value of the effective refractive index or a reduction of the grating length. As it is hard to believe that the Bragg grating contracts at a high temperature, the blue drift should be mainly correlated with a slow change of the effective refractive index of the fiber substrate and/or the grating. The dopant diffusion could lead to a blue drift but, according to published reports, by the time the blue drift starts, , the diffusion process will have already been completed as it reaches equilibrium in less than 100 h . MoreoveFor FBGs to have a future as temperature sensors in the 900–1000 °C range, the phenomenon of Bragg wavelength drift needs to be addressed. According to the review of published results and our recent experiments presented in this paper, the drift exhibits two types of behavior: a fast red drift in the first 100–200 h and a slow blue drift after a “no-drift” period. As it seems that the red drift is caused by the release of frozen-in stresses generated during the fiber manufacturing process, its negative effect on the temperature measurement can be somewhat mitigated by pre-annealing the FBG sensor at a higher temperature. In our tests, annealing FBGs at 1000 °C for a few hundred hours reduced the wavelength drift at 900 °C to ~0.5 pm/h (0.03 °C/h), which, however, is still too high for accurate long-term temperature measurements. The same trend was reported in the literature when pre-annealing FBGs at 1100 °C and 1200 °C helped reduce wavelength drift at a lower temperature. The slow blue drift (0.1–0.5 pm/h rate) is more difficult to eliminate because of its elusive nature. However, it is probably possible to correct for the slow blue drift numerically as this phenomenon seems to be more predictable compared with the red drift. We would also like to emphasize that the Bragg wavelength drift at high temperatures should not affect FBG-based measurements performed on short time scales.


Self-assembly of proteins into amyloid fibrils is a hallmark of various diseases, including Alzheimer’s disease (AD) and Type-2 diabetes Mellitus (T2DM). Aggregation of specific peptides, like Aβ42 in AD and hIAPP in T2DM, causes cellular dysfunction resulting in the respective pathology. While these amyloidogenic proteins lack sequence homology, they all contain aromatic amino acids in their hydrophobic core that play a major role in their self-assembly. Targeting these aromatic residues by small molecules may be an attractive approach for inhibiting amyloid aggregation. Here, various biochemical and biophysical techniques revealed that a panel of tryptophan-galactosylamine conjugates significantly inhibit fibril formation of Aβ42 and hIAPP, and disassemble their pre-formed fibrils in a dose-dependent manner. They are also not toxic to mammalian cells and can reduce the cytotoxicity induced by Aβ42 and hIAPP aggregates. These tryptophan-galactosylamine conjugates can therefore serve as a scaffold for the development of therapeutics towards AD and T2DM. Paul et al. provide a rational design strategy to develop dual inhibitors, in the form of tryptophan-galactosylamine conjugates, against amyloid formation and aggregation of Aβ42 and hIAPP peptides. These conjugates are not toxic to mammalian cells and reduce cytotoxicity of Aβ42 and hIAPP aggregates, hence potentially could serve as scaffolds for development of Alzheimer’s disease therapeutics. Despite arising from different genetic, environmental and regulatory factors, most proteinopathies exhibit sub-cellular and molecular similarities, characterized by the accumulation of misfolded proteins that often, self-assemble into thermodynamically highly stable amyloid fibrils, which are resistant to proteolytic degradation and lack functionally stable amyloid fibrils7.Aberrant protein folding and consequent protein aggregation are hallmarks of a group of pathological conditions termed proteinopathies6. AD is the most common form of dementia in ageing populations9. A major triggering neuropathological cause of AD is the misfolding and accumulation of Aβ42, a hydrophobic peptide of 42-amino acids that is prone to self-assemble in the brain into toxic extra-cellular amyloid fibrils termed senile plaques10. Protein self-assembly is also a key feature in T2DM11. In this disease, pathological deposits of the peptide amylin, also called hIAPP, are found in the pancreatic islets of Langerhans of T2DM patients12. hIAPP is a 37-amino acid neuroendocrine peptide hormone produced by β-cells together with insulin14. When high insulin is required, such as in insulin deficiency, expression of hIAPP significantly increases, enhancing its propensity to aggregate into cytotoxic amyloid assemblies resulting in loss of β-cells and β-cell mass15.Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM) are prime examples of proteinopathies17. Hyperglycemia increases the chance of developing AD by at least 2-fold by contributing to the accumulation of amyloid plaques on brain lesions18. AD brains are less capable of glucose uptake from the surroundings, resembling a condition of insulin resistance19. In both T2DM and AD, oxidative stress is exacerbated, mitochondrial functions are impaired and neuron integrity compromised23.Epidemiological studies indicate comorbidity of AD and T2DM25. In both Aβ42 and IAPP, the minimal aggregating sequence is a core of hydrophobic amino acids containing aromatic residues, KLVFFA and NFGAIL, respectively28. Aromatic amino acids have been identified as crucial in the formation of various amyloid structures. π-π stacking, rather than mere hydrophobicity, was shown to provide energy, order and directionality to promote amyloid assembly31. Based on these insights, peptide-based inhibitors were designed to contain aromatic residues whose side chains can intercalate the target amyloid aggregate and partially replace the original amino acids in π–π stacking36. Along the same lines, small aromatic molecules, e.g. polyphenols, have been amply studied as inhibitors of amyloid aggregation, where the aromatic ring appears to similarly interfere with amyloid self-assembly41.Intervening with the initial steps of protein misfolding and aggregation can thus be both a prophylactic measure and a means for modifying the course of these diseases46. However, a major limitation in the drugability of such molecules is their poor solubility in aqueous media49: Attaching a hydrophilic moiety for increasing solubility may enhance the overall therapeutic capacity of a drug48. Functionalization with glycans has been proven to enhance solubility, half-life and specificity of certain drugs in vivo50. The conjugation of glycans to peptides increases the overall interaction surface of the hybrid molecules, therefore increasing solubility by expanding the number of possible interactions with the solvent53. Along the same rationale, aromatic molecules, which display high potential as anti-amyloidogenic agents, but are by themselves hydrophobic, were conjugated to mannitol and glucosamine54.Combining the aromatic elements of amino acids and small molecules has also been attempted for amyloid inhibition. For example, a naphthoquinone–tryptophan hybrid was shown to be effective towards various amyloids  in vitro as well as in vivo56. Galactose , one of the most abundant monosaccharide in the human body, plays a vital role in numerous biological processes, modulating and mediating them58. To be able to speculate on the mechanistic role of galactose, we have included in the study GalNH2 and GalNAc modifications at C2 of galactose influencing its charge density.Here we examined this concept by synthesizing tryptophan-galactosylamine hybrid molecules and tested their ability to inhibit the aggregation of Aβ42 and hIAPP. Tryptophan was shown to be highly effective in intercalating the fibrils of various amyloidogenic proteins for inhibiting their aggregationsBy means of Thioflavin T (ThT)-binding assay, circular dichroism (CD) spectroscopy, transmission electron microscopy (TEM) and Congo red birefringence, we show that the presence of galactose is key for the improved efficiency of tryptophan as amyloid inhibitor. The tested compounds are not toxic to mammalian cell lines. These results suggest that tryptophan-monosaccharide may be developed as disease-modifying therapeutics to target simultaneously Aβ42 and hIAPP.2 and NAc groups. For simplicity, the hybrid molecules resulting from the conjugation of tryptophan and these sugars are hereafter referred to as WGal, WGalNH2 and WGalNAc. As a negative control, the dipeptide formed by two tryptophan, WW was included. The structure of these hybrids is shown in Fig. To examine the possibility of combining an amino acid and a glycan into a potentially improved amyloid inhibitor, we conjugated tryptophan to the N-glycoside 1-amino-1-deoxy-β-D-galactose  and to its variants derived from the substitution of the OH on C2 with NH60. The curve reached a plateau after ca. 8 h, indicating the completion of amyloid fibril formation. Next, Aβ42 was co-incubated with increasing doses of the tested hybrid molecules at peptide:hybrid molar ratios of 5:1, 1:1 and 1:5  was caused by the hybrid molecules towards either cell line, even at the highest concentration of 250 µM  with either human neuroblastoma (SH-SY5Y) or with human embryonic kidney (HEK-293) cell lines and assessed cell viability by XTT assay µM Fig. . However50 values for Aβ42 and hIAPP were found to be ~13 µM and ~10 µM, respectively, as compared to untreated control cells, in agreement with previous reports79. Various doses of the hybrid molecules  were added separately to pre-aggregated Aβ42 (13 µM) or hIAPP (10 µM). The mixtures were then independently applied to the respective cells for 24 h, and cell viability was further measured by XTT assay  to allow fibril formation. The peptide assemblies were then incubated for further 24 h with SH-SY5Y or HEK-293 cells and cell viability was monitored by XTT assay   Fig. . In agreely Fig. .2 and WGalNAc incremented the viability of 18 ± 5%, 61.7 ± 5%, 53 ± 5%, and 64 ± 4% respectively   Fig. . The calely Fig. .These results indicated that the hybrid molecules significantly reduced the cytotoxic effect induced by Aβ42 and hIAPP fibrils. WGalNAc was the most effective among them.In this work, we evaluated the potential of a new class of highly-soluble tryptophan-galactosylamine hybrid molecules to alter the progression of the aberrant aggregation of the disease-associated peptides Aβ42 and hIAPP, both in vitro and in mammalian cell lines.80. Indeed, tryptophan has been shown to be directly involved in the abrogation of protein aggregation by targeting the aromatic recognition interfaces of amyloidogenic proteins42. Tryptophan derivatives have been employed in the design of several inhibitors of amyloid formation. For example, tryptophan-containing peptides have been employed to reduce Aβ42-derived toxicity in animal models81. Tryptophan-coated gold and silver nanoparticles have been shown to inhibit both spontaneous and seed-induced aggregation of insulin82. Naphthoquinone–tryptophan hybrids displayed promising potential for mitigating the amyloidogenicity of several proteins and peptides83.The amino acid tryptophan was chosen in this study because it was reported to display the most amyloidogenic propensity among the proteinogenic amino acids, suggesting a potentially strong involvement of its aromatic side chain in the molecular associations leading to the formation of amyloid fibrils85.The present work aimed at improving the drugability of tryptophan by combining its ability to target amyloid species with a component that would confer the capacity to dissolve the target protein aggregates. Thus, we postulated that the conjugation of tryptophan to a glycan unit could increase solubility and target selectivity of the hybrid molecule88. For example, galactosylated nanocarriers have been shown to improve site-specific delivery of siRNA and anticancer drugs86. Indeed, galactose has been employed in studies for the treatment and diagnosis of several diseases, as well as vaccine development91. D-galactose, together with D-glucose, are actively transported across the blood-brain barrier (BBB), making these hexoses highly attractive for the specific delivery of drugs to the brain92. Galactose-conjugated dopamine was found to have better BBB permeability, bioavailability, and therapeutic effects against Parkinson’s disease95. Finally, galactose is a preferred glycan candidate for targeting hIAPP because, due to hepatic metabolism, insulin and glucose level responses to galactose are lower than for glucose97.The glycan galactose is involved in numerous biological processes, hence galactosylation is considered as a useful tool for delivery of therapeuticsTaken together, this evidence points to tryptophan and galactose as desirable building blocks for the design of potential hybrid molecules against amyloid formation.98. The formation of oligomers in the absence of other pre-existent larger peptide species is refer to as primary nucleation99. These oligomers can grow into fibers by the addition of other oligomers (elongation) or monomers (secondary nucleation)60. The kinetics of amyloid formation generally follows a sigmoidal trend, in which a stable lag phase, an exponential phase and a plateau, the stage by which no more monomers are available, can be distinguished101. According to the mechanism of action of a potential inhibitor, any of the phases of the aggregation process may be effected. All our in vitro analyses demonstrated an appreciable amyloid inhibitory effect on both Aβ42 and hIAPP by all the tryptophan-galactosylamine hybrids. The data collected for WGal, WGalNH2 and GalNAc show that there is no kinetic effect caused by the inhibitors on primary nucleation or elongation . Instead, the plateau levels of both Aβ42 and hIAPP are significantly reduced by the presence of the hybrid molecules. To speculate on the mechanistic role that these inhibitors are playing on the aggregation of the target peptides, it is important to bear in mind that there are many ways by which small molecules may model the aggregation pathway103. Among these, it has been reported that various inhibitors reduce the amount of amyloids by redirecting the aggregation towards alternative off-path products, rather than preventing the self-assembly of the monomers106. We believe that this is the case for the hybrid molecules studies here: the lack of an obvious kinetic effect on primary/secondary nucleation or elongation suggests the sequestration of the monomers and oligomers to a parallel alterative pathway that does not lead to amyloid formation but to smaller and often amorphous assemblies. This hypothesis is supported by the ca. 50% reduction of ThT-positive aggregates in the presence of the inhibitors; by the variation in Aβ42 and hIAPP’s secondary structures; by the lack of fiber-like assemblies as shown by TEM and Congo red birefringence; and by the significant lower cellular toxicity.At the core of the amyloidogenic process there are conformational changes of the native, soluble peptides into unstable oligomers that subsequently assemble into insoluble protofibrils and fibrils2, as indicated by the p-value calculations . These results could be explained by the amyloidogenic nature of tryptophan and by its ability to self-assemble into fibrils, resulting in an additive aggregation level80. The co-presence of aggregating peptides may have functioned as further nucleation agent for the π–π stacking of tryptophan, which, on its end, could also have consequently induced further Aβ42 and hIAPP aggregation into mature amyloid fibrils. This effect, however, did not translate into an increment of toxicity in the in-cell assays: albeit not as efficiently, WW, like the hybrid molecules, reduced the cytotoxicity induced by extra-cellular pre-aggregated Aβ42 and hIAPP. To explain the partial inhibitory effect of WW on Aβ42 and hIAPP-induced toxicity, we postulate that WW may have driven the aggregates of these peptides towards larger bundle of fibrils, which are reported to be less toxic than smaller oligomeric forms114. WW’s effect on cytotoxicity, however statistically significant, is very weak when compared to the hybrid molecules: EC50 of all hybrids are between 2.5 and 5-fold lower than the one calculated for WW.Alongside the designed hybrid molecules, all experiments performed in this work included the dipeptide WW, here referred to as “negative control” which allowed to assert the effect of the galactosylamine derivatives in comparison to the tryptophan alone. However, in some circumstances, WW displayed a stimulatory effect on the aggregation of Aβ42 and hIAPP. This behavior became more pronounced and statistically significant . hIAPP was purchased from Peptide 2.0 (USA). The inhibitors  were purchased from Aldlab (USA). WW was purchased from GL Biochem Shanghai (China). Lipids were purchased from Avanti Polar Lipids (USA). All chemicals and reagents were of analytical grade. Unless otherwise stated, all chemicals were obtained from Sigma-Aldrich .Aβ42 and hIAPP were monomerized by a 10 min pre-treatment with HFIP and the solvent was evaporated using a Speed Vac. The resulting thin films were dissolved in PBS  and sonicated for 5 min to get a stock concentration of 50 µM, which immediately diluted to required concentration for an experiment. Stock solutions of ThT (4 mM in PBS) and hybrid molecules (10 mM in PBS) were prepared. All the stock solutions were diluted according to the requirement.44. For fibril disassembly assays, Aβ42 and hIAPP were first allowed to self-assemble until a ThT-associated plateau was reached; the hybrid molecules were then added separately to designated wells , and the fluorescence values were further followed until a new plateau was reached. Kinetics fluorescence data were collected at 37 °C in triplicate using Infinite M200 microplate reader , with measurements acquired at 15 min intervals. Excitation and emission wavelengths were set at 440 nm and 485 nm, respectively.For the aggregation kinetics, the stock solutions of Aβ42 or hIAPP were diluted in a 96-well black plate so that the final mixture (100 µl) contained 10 µM peptide and 20 µM ThT in 100 mM PBS. The experiment was performed according to the previous report42 and hIAPP) were placed in a quarz cuvette (path length 1 mm) and CD spectra were recorded on a Chirascan spectrometer between 190–260 nm. The blank was subtracted from the acquired CD spectra. For the CD study in the presence of the tryptophan-galacosylamine molecules, the spectra of hybrid molecules were also recorded separately and subtracted from the respective sample spectra. The normalized data were plotted using Origin pro 2015 software.To analyze the secondary structure of the aggregated or disassembled peptides, 300 μL samples (10 µM of AβSamples (10 µL) were placed for 2 min on 400-mesh copper grids covered with carbon-stabilized Formvar film (Electron Microscopy Sciences (EMS), Hatfield, PA). Excess fluid was removed, and grids were negatively stained with 2% uranyl acetate solution (10 µL) for 2 min. After excess fluid removal, the samples were visualized using a JEM-1400 TEM (JEOL), operated at 80 kV.5 cells/mL) were cultured in 96-well tissue microplates (100 µL/well) and allowed to adhere overnight at 37 °C. The conjugate molecules were dissolved in DMEM:nutrient mixture F12 (Ham’s) (1:1)  at different concentrations. The negative control was prepared as medium without hybrid molecules and treated in the same manner. 100 µL of medium with or without hybrid molecules were added to each well in triplicate. Following incubation for 24 h at 37 °C, cell viability was evaluated using the 2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide (XTT) cell proliferation assay kit  according to the manufacturer’s instructions. Briefly, 100 µL of activation reagent was added to 5 mL of XTT reagent, followed by the addition of 50 µL of activated-XTT solution to each well. After 2 h incubation at 37 °C, color intensity was measured using an ELISA microplate reader at 450 nm and 630 nm. Results are presented as mean and mean standard error. Each experiment was repeated minimum three times.The SH-SY5Y and HEK-293 cell lines  and type to 2 , according to reported methods121. From the XTT viability assays, EC50 and Emax values were determined from the dose-response curves of each tested molecule using non-linear least-square data fitting, according to literature122.For the quantitative data derived from ThT and XTT assays, statistical significance, expressed as Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary DataPeer Review FileReporting Summary


Several years ago, the SUM panel of human breast cancer cell lines was developed, and these cell lines have been distributed to hundreds of labs worldwide. Our lab and others have developed extensive omics data sets from these cells. More recently, we performed genome-scale shRNA essentiality screens on the entire SUM line panel, as well as on MCF10A cells, MCF-7 cells, and MCF-7LTED cells. These gene essentiality data sets allowed us to perform orthogonal analyses that functionalize the otherwise descriptive genomic data obtained from traditional genomics platforms. To make these omics data sets available to users of the SUM lines, and to allow users to mine these data sets, we developed the SUM Breast Cancer Cell Line Knowledge Base. This knowledge base provides information on the derivation of each cell line, provides protocols for the proper maintenance of the cells, and provides a series of data mining tools that allow rapid identification of the oncogene signatures for each line, the enrichment of KEGG pathways with screen hit and gene expression data, an analysis of protein and phospho-protein expression for the cell lines, as well as a gene search tool and a functional-druggable signature tool. Recently, we expanded our database to include genomic data for an additional 27 commonly used breast cancer cell lines. Thus, the SLKBase provides users with deep insights into the biology of human breast cancer cell lines that can be used to develop strategies for the reverse engineering of individual breast cancer cell lines. The types of genomic data that are now available to clinicians and laboratory scientists include sequencing-based analyses that show the point mutations that occur in breast cancer, copy number data that provide information on the oncogenes activated by gene amplification as well as the tumor suppressor genes inactivated by point mutations or homozygous deletion, gene expression data from RNA-sequencing and gene expression arrays, and most recently, proteomics and phospho-proteomics data. In addition, a number of tools and databases have been developed, such as the CaBio Portal6, whereas others have been genome-scale and made use of libraries of varying complexities9. We have performed genome-scale shRNA screens for the SUM breast cancer cell line panel and some of the results from those experiments have been reported12. These functional screens have resulted in the generation of long lists of genes that have been found to play a direct role in either the proliferation or survival of specific human breast cancer cell lines. Moreover, when these sets of essential genes are analyzed with respect to the descriptive genomic data sets described above, the result is a functionalization of the genomic data that sheds light on the biology of individual breast cancer cell lines. The functionalization of genomic data is particularly important with respect to predicting the sensitivity or resistance of breast cancer cells to targeted drugs. Targeted drugs are effective when they inactivate a functional driver gene, but they are ineffective when they target a passenger gene, and this notion is supported by results of numerous clinical trials, both positive and negative. Because gene essentiality screens effectively distinguish driver from passenger genes, regardless of expression level or genomic status, these analyses are powerful ways to identify druggable targets in breast cancer cells. Thus, functional genomics has the potential to make accurate predictions about targeted drugs that are likely to have the largest impact on cancer cells with the highest therapeutic index.As powerful as these tools and data sets are, they are descriptive in nature and the inferences and conclusions that can be drawn from them are, as a result, correlative. Recently, a number of laboratories, including our own, have performed shRNA or CRISPR-Cas9-based gene essentiality screens on a large number of human breast cancer cell lines. Some of these screens have been focused on specific gene sets, such as the kinome12. More recently, we developed a series of web-based tools that allow breast cancer researchers who work with these cell lines to quickly and easily mine the data to identify the functional oncogenes, the most essential biological pathways, and the genes within those pathways that mediate growth and survival of the cells, as well as the best druggable targets in each cell line based on essentiality and druggability of each gene. Other data mining tools allow researchers to determine the status of any annotated gene in the genome in any cell line, along with its expression status at the mRNA and protein level, and genomic alterations that may be present in the gene. Finally, all of these data and data mining tools are presented in the context of a Knowledge Base that provides critical information on the derivation of each cell line, the patient from which the cells were derived, and the proper conditions for maintaining each cell line. Recently, we have expanded this Knowledge Base to include 27 additional breast cancer cell lines for which reliable genomics and functional screening data are available, and the data mining tools originally developed for the SUM lines can be used for a total of 40 breast cancer cell lines.To take full advantage of these functional genomics data sets, new tools need to be developed that allow breast cancer researchers to quickly identify the most essential genes, oncogenes, pathways, and druggable targets for any cell line, and then ultimately translate that knowledge to make predictions about breast cancer specimens from primary sites or metastatic lesions. Over the past 5 years, our laboratory has performed genome-scale shRNA screens on the SUM breast cancer cell lines developed in our laboratory, as well as on MCF10A cells, MCF-7 cells, and MCF-7LTED cellsThis Breast Cancer Cell Line Knowledge Base and the data mining tools contained within allow for rapid functional genomic analysis not only for the SUM breast cancer cell lines but for all breast cancer cell lines for which validated functional screens have been performed.16, as were breast cancer cell lines with HER2 amplifications for the development of HER2-targeted drugs24. More recently, palbociclib was identified in a drug screen using a large panel of breast cancer cell lines25. Despite the importance of breast cancer cell lines in the development of modern therapeutic modalities for breast cancer, most researchers know relatively little about the cell lines they work with, and thus, the full potential of the large panel of breast cancer cell lines that currently exists has not been fully realized. In attempt to address this gap in our understanding, and to increase the power and importance of breast cancer cell lines in research, we set out to develop a knowledge base that allows researchers using the SUM breast cancer cell lines, as well as other commonly used breast cancer cell lines, to have ready access to the genomic and functional genomic data that have been generated from these cells, and to be able to quickly and easily mine these complex data sets. The SUM Breast Cancer Cell Line Knowledge base is the result of these efforts and provides a gateway for the functional genomic analysis of breast cancer cell lines.Human breast cancer cell lines have been and continue to be a mainstay of breast cancer research worldwide. Indeed, breast cancer cell lines have played a key role not only in helping to elucidate the fundamental biology of breast cancer but also for the development of virtually every drug that is used to treat breast cancer patients. The MCF-7 cell line was critically important to the development of the hormonal therapies used to treat patients with estrogen-receptor positive breast cancer26. In addition, we incorporated into the database the list of targeted drugs that are linked to specific genes from the Genomics of Drug Sensitivity in Cancer database. We then designed and launched a series of web-based tools that allow these data sets to be mined in ways that shed light on the deep biology of each cell line and suggest targeted drug strategies that are likely to be effective in each of the lines.There were three overarching goals in the original development of the SLKBase: (1) to provide a rich source of information for anyone working with any of the SUM breast cancer cell lines, (2) to give researchers ready access to the large genomic data sets that have been developed with these cells, and (3) to allow researchers to perform orthogonal analyses of the various genomics data sets that we and others have obtained from the SUM lines. To build a platform for analysis of genomic data sets from the SUM lines, we first built a MySQL database that contains copy number data derived from array comparative genomic hybridization, gene expression data derived from Illumina bead arrays and more recently from RNA sequencing, point mutation data derived from whole-exome sequencing, and finally, data from the genome-scale shRNA screens for each of the SUM lines and for MCF10A, MCF-7, and MCF-7LTED cellsoncogene signatures for a given cell line, and we have reported on these for some of the SUM lines in previous publications12. We thus wanted to develop a tool that would allow anyone to ascertain the several types of oncogene signatures for any breast cancer cell line for which these genomic data sets are available, and this is now available on the SLKBase. By using the Oncogene Signature Tool, one can choose a breast cancer cell line and immediately identify three types of oncogene signatures. The first is the Candidate Oncogene Signature, which comprises genes that are genomically altered in the cell line, and are considered to be bona fide human oncogenes as indicated in the OncoKB database27. Thus, this list shows all candidate oncogenes that are genomically altered in the cell line, and their score in the shRNA screen provides information on the essentiality of each altered gene in the cell line. The second gene set is the Overall Oncogene Signature, which comprises any gene that is genomically altered in the cell line of interest that was also a hit in the functional screen, regardless of whether they are considered bone fide oncogenes. Thus, any gene that is genomically altered, by either gene amplification or point mutation, and was a hit in the functional screen is reported along with the expression level of the gene and its potential druggability. Finally, the Functional Oncogene Signature is the synthesis of the first two gene sets and shows the genes that are genomically altered, considered to be bona fide oncogenes, and were hits in the functional screen, along with their expression levels and druggability. The Candidate Oncogene Signature for the SUM-185 breast cancer cell line is shown in Table FGFR3, PIK3CA, and BCL2L1), and we have shown previously that targeting these oncogenes using low doses of appropriate targeted drugs yields dramatic synergistic and cell line-specific lethality12. The Candidate Oncogene Signature for the SUM-190 cell line is shown in Table 29 and others have published35, the SUM-190 cell line, derived from a patient with inflammatory breast cancer, has a HER2 gene amplification, and HER2 is overexpressed at the mRNA and protein level. And yet, HER2 was not a hit in the functional screen for these cells. This is contrast to the SUM-225 cells, which also has HER2 amplification29 and for which HER2 was a hit in the functional screen  are highly sensitive to this drug. Thus, in SUM-190 cells, for which HER2 lies upstream of PI3′Kinase signaling, PIK3CA is a better druggable target than HER2, a result predicted by the shRNA screen data. Interestingly, the HER2-amplified SUM-225 cell line is also highly sensitive to Alpelisib, an observation made with other HER2-amplified breast cancer cell lines, as can be observed using the Functional-Druggable Target tool on the SLKBase. The concentration–response curves for Alpelisib across the SUM cell line panel are shown in Supplementary Fig. One of the most powerful applications of genome-scale shRNA screens is the functionalization of genomic alteration data that are derived from sequencing or array-based applications. It is well-known that breast cancers, like most cancers, are genomically complex and that most of the genomic alterations that occur do not contribute directly to the malignant potential of the cells and are therefore poor drug targets. Thus, by combining data derived from essentiality screens with data derived from copy number analysis, gene expression analysis, and exome sequencing, one can quickly reduce the complexity of these data sets and identify the driving oncogenes in each cell line. We refer to the gene sets that are derived from such an analysis as the 12). Thus, elucidating the oncogene signatures of breast cancer cell lines is the starting point for developing targeted drug strategies that will be highly effective when used at low concentrations, and thus yield a high therapeutic index.The concept of oncogene addiction predicts that targeting functional-druggable oncogenes has a profound and specific effect on proliferation and/or survival in cells with those driving oncogenes. The oncogene signatures for the SUM lines make predictions about sensitivity to oncogene-targeted drugs, and these predictions are borne out by drug sensitivity data, as shown in Fig. https://www.cancerrxgene.org)36. This database lists all of the targeted drugs that have been tested experimentally against a panel of nearly 1000 cancer cell lines. The functional-druggable signature tool returns a list for each breast cancer cell line of genes that are both essential as determined by their hit status in the screen and druggable using a targeted agent. The functional-druggable signatures for three of the SUM lines are shown in Table Z-scores for this drug in each of the SUM lines are shown in Fig. 2a26 and the red bars indicate the cell lines for which either BCL2L2 or BCL2 (SUM-44) was a hit in the screen, and shows a good association between drug sensitivity and essentialness as determined in the screen. The concentration–response curves used to calculate the IC50 concentrations for Navitoclax for each of the SUM breast cancer cell lines are shown in Supplementary Fig. One of the advantages of genome-scale shRNA screens is that they identify a large number of essential genes, most of which are not genomically altered, and, therefore, not bona fide oncogenes. And yet, because these genes are essential to the growth and/or survival of the cells, they can be good drug targets as well. In order to fully leverage the data derived from our genome-scale functional screen, we created the Functional-Druggable Signature tool, which merges the shRNA or CRISPR screen hit data with data derived from the Genomics of Drug Sensitivity in Cancer database . Work currently in progress is aimed at performing a rigorous statistical analysis of the association between essentiality and drug sensitivity for all targeted drugs across the entire cell line panel.To begin to test this hypothesis more rigorously, we developed a version of the functional-druggable signature tool for the 27 additional breast cancer cell lines for which there are both functional genomic data from Project Achilles, and drug sensitivity data from the Genomics of Drug Sensitivity in Cancer database. Using this tool, one can directly examine the relationship between essentiality as determined by the screen hit score (CERES score) and the FGFR3 is a functional-driving oncogene activated by gene amplification, is highly dependent on this pathway for proliferation and survival. By contrast, other cell lines are less dependent on this pathway, and indeed, the SUM-229 cell line (lower left panel) has little or no reliance on this pathway for proliferation or survival as indicated by the small number of screen hit genes that map to the pathway.Another way to gain insight into the biology of human breast cancer cell lines is to use the screen hit data to identify the most important biological pathways for each cell line. To accomplish this goal, we developed the KEGG Pathway Engine, which maps the genes that were hits in the functional screen onto KEGG pathways. The KEGG Pathway Engine allows users to pick any KEGG pathway and determine its importance in any cell line in the database, obtain a visual picture of the pathway of interest with screen hits in the pathway displayed, and view the data associated with each hit gene in the pathway. A separate feature of this tool is the ability to view gene expression data for the specific cell line and pathway and compare that to the screen hit data. Figure KRAS-transformed cell line that falls into the basal/claudin-low subset of triple-negative breast cancers and has been predicted by others to be enriched for expression of genes in the TGF-beta pathway40. Thus, the Pathway Engine result is not only consistent with this prediction but also identifies the specific genes within the pathway that are most essential for these cells. The WNT signaling and TGF-beta signaling pathways were also found to be important in SUM-1315 and SUM-159 cells, the latter of which is also a MYC/HRAS transformed cell line. Thus, the KEGG pathway engine and Pathway Analysis tools, coupled with the functional-druggable signature tool, help to identify strategies and drug targets even in breast cancer cells that do not express functional-druggable oncogenes.To increase to power of the KEGG pathway engine, we developed a Pathway Analysis tool that calculates the relative essentialness of all KEGG pathways and rank-orders them based on the level of enrichment of the screen hit data to the pathway. The algorithm also takes into account the proportion of the genes in the pathway that were screen hits, as well as the relationship of hit genes in the pathway to each other, such that screen-hit genes that are directly linked to other screen hits in the pathway receive more weight. Table BCL2L1 gene, which is of developing interest as a therapeutic target in breast cancer, are shown in Table BCL2L1 is amplified, overexpressed, and a hit in the screen in SUM-185 cells. This gene, which is expressed at relatively normal levels in SUM-52, SUM-190, and SUM-149 cells, was a hit in the screen in these cell lines as well, and they are, indeed, sensitive to the targeted drug Navitoclax  and EIF4EBP1 as important driver genes from this amplicon in these cells48. In addition, SUM-44 cells overexpress BCL2, which was a strong hit in the screen, and the cells are exquisitely sensitive to Navitoclax and Venetoclax. CDK6 was also a strong hit in the shRNA screen in SUM-44 cells, and these cells are sensitive to palbociclib, as predicted from the functional druggable signature for these cells. By exploring important KEGG pathways, one can see that SUM-44 cells express a number of cytokines and chemokines, many of which, such as CCL1, LIFR, CCL25, and others, were strong hits in the functional screen, indicating their importance in the proliferation and survival of these breast cancer cells. Finally, these cells, having been derived from a patient with lobular breast cancer, have a point mutation in the CDH1 gene rendering them E-cadherin null, and thus, a good model of lobular breast cancer47. These findings demonstrate that overexpression and activation of the ER is just one feature of the SUM-44 cell line, and this phenotype occurs in the context of other molecular and cellular features important to the biology of the cells and the patient from whom they came. Thus, the SLKBase can be used to explore the biology of individual breast cancer cell lines, which yields novel and important observations on breast cancer cell lines that can be used to elucidate new strategies for reverse engineering of human breast cancer cells.Rather than considering the use of breast cancer cell lines because of one or two specific characteristics, it is now possible to consider cell lines as individual breast cancer patients, and use functional genomics to identify, in an unbiased manner, the most important genes, pathways, and druggable targets for each line. Such a systems-level approach can shed new light on many breast cancer cell lines. For example, the first line that we developed in our laboratory was SUM-44PE51 is the primary driving principle behind the orthogonal omics strategy that is key to the SLKBase. The principle of oncogene addiction proposes that cancer cells have one or more driving oncogenes to which the cells are addicted for their proliferation and survival, and the correctness of this principle has been shown in many laboratory and clinical studies. Based on this principle, we predicted that we could identify the functional-driving oncogenes in any cell line by an orthogonal analysis of gene amplification, gene expression and point mutation data, with shRNA screen data, which shows all of the genes in a given cell line that are essential for their growth and survival. Furthermore, the principle of oncogene addiction predicts that, for those functional-driving oncogenes that are druggable, cells should be exquisitely sensitive to drugs that target the products of those driving oncogenes. The results of our studies with the SUM lines support the predictions made by the principle of oncogene addiction.Oncogene addiction52 highlights the importance of linking gene essentiality data with drug sensitivity for the proper clinical development of targeted drugs.A second driving principle that underlies our approach to functional genomics is that since screen hits identify genes essential for growth and/or survival of the cancer cells, cells that express essential genes that are druggable should be sensitive to drugs that target those genes. For this reason, the SLKBase not only provides data on functional oncogenes in each breast cancer cell line, it also provides functional-druggable signatures for each cell line. Indeed, results of our experiments support the connection between essentialness as determined in the shRNA screens and drug sensitivity for a number of targeted drugs such as EGFR inhibitors, BCL2/BCLXL inhibitors, p38 MAP kinase inhibitors, PLK1 inhibitors, palbociclib, and others. In addition, our KEGG pathway analysis tools help to solidify the connection between functional druggable targets and essential pathways in individual cancer cell lines. The recent paper by Lin et al.HRAS point mutation, and in the SUM-159 screen, this gene was ranked 2378 out of approximately 15,000 genes queried in the screen. Had we chosen a more liberal cut-off point; this gene could have been considered a hit. For this reason, in all of the data that are returned on specific genes using the data mining tools in the SLKBase, the actual screen hit ranking is provided along with the hit status, so investigators can see for themselves how “essential” any gene is in any given cell line. We have identified other examples of false negatives for the SUM lines that are important to discuss. For example, SUM-44 cells express very high levels of ESR1 mRNA and ER protein, both of which can be seen on the SLKBase. Furthermore, we have previously shown that while SUM-44 cells are relatively estrogen-independent and Tamoxifen-resistant, these cells are sensitive to ER degraders such as fulvestrant, and knock-down of ESR1 using shRNAs resulted in profound loss of cell viability. Thus, the screen hit ranking of ESR1 of 4857 in SUM-44 cells appears to be a false-negative result and suggests that the shRNAs that target ESR1 in the Cellecta library did not have a strong enough effect on knock-down of the very high mRNA levels to result in a sufficiently high drop-out rate to score as a hit in the screen.One concern that investigators have with genome-wide screens is the possibility of false-positive results that point to genes that are incorrectly identified as essential. In our hands, using the Cellecta library of shRNAs and the statistical method we developed for analyzing the screen data, false positives have not been a significant issue. So far, we have never failed to confirm a screen hit using individual shRNA constructs that target putative essential genes. However, we do consider false negatives to be an issue with our screen data. The cut points that we chose to determine the genes considered to be hits in the screen typically yield approximately 1000 hits per cell line. We intentionally chose a conservative cut point so as to minimize false positive hits. The consequence of this decision is more false-negative results that one has to be cautions of. For example, SUM-159 cells have a classic n of 1 problem, and truly make targeted cancer therapy precise and effective.In summary, our in-depth analysis of the SUM breast cancer cell lines, and more recently, other breast cancer cell lines, reinforces the importance of taking a systems-level approach to understanding breast cancer cell lines, and not lose sight of the fact that each cell line was derived from an individual patient with a specific set of molecular characteristics. As part of this approach, it is critical that investigators obtain and work with cancer cell lines appropriately to ensure that they are working with the cells they think they are, and to ensure that phenotypic drift is minimized, as any alteration in culture conditions from those originally used to develop the cell line can result in selection of subpopulations present within the cell line. By taking such measures, cancer cell lines are stable models of the type of breast cancer that was experienced by the patients from which they came, making it possible to use these model systems to develop novel and innovative reverse-engineering strategies for each cell line (patient) and ultimately use those strategies to solve the https://sumlineknowledgebase.com/?page_id=350). Briefly, SUM-44 and SUM-52 are luminal B cells; SUM-102, SUM-149, SUM-159, SUM-229, and SUM-1315 are triple-negative breast cancer cells. More specifically, SUM-159 and SUM-1315 cells map to the claudin-low subtype. SUM-190 and SUM-225 cells are HER2-positive breast cancer cells, and SUM-185 maps to the androgen-receptor enriched sup-type of breast cancer cells.All inhibitors were purchased from Selleckchem. The SUM breast cancer cell lines were maintained as described previously. MCF10A cells were a gift from Dr. Herb Soule at the Michigan Cancer Foundation. The molecular subtypes of each of the SUM lines along with additional information regarding each line are presented in the SLKBase (2 buffer (0.01 mM HEPES and 0.015 mM MgCl2) for 5 min, and lysed for 10 min with ethyl hexadecyldimethylammonium solution. For most of the cell lines, IC50s and the standard deviations were determined using GraphPad. For the SUM-229 and SUM-44 cell lines that were resistant to Alpelisib and as a result did not yield a large enough change in cell growth to result in a sigmoid curve interpretable by GraphPad, a local polynomial regression was used (LOESS with a span = 1) to fit a local regression curve over the range of data and determine the IC50 value. The 95% prediction interval was calculated around the curve, and the interval at the point where cell concentration was predicted to be 50% of the starting value (coinciding with the IC50 concentration) was inverted to derive the error for the IC50 value.Cells were plated in 24-well plates at a density of 15–30,000 cells per well. Cells were allowed to recover for 4 days before being treated in triplicate with the indicated inhibitors or DMSO control every 24 h for 4 days. On the fifty day, cell number was determined by harvesting and counting nuclei on a Z1 Coulter Counter . To prepare nuclei for counting, cells were washed three times with PBS, incubated on a rocker table with 0.5 ml per well HEPES/MgClMicroarrays with an average resolution of 35 kb (Agilent Human Genome CGH Microarray 44k chip) were hybridized after direct labeling of DNA with fluorescent dyes. DNA extraction was performed using standard column purification (Qiagen) and normal human female DNA was used as the reference. Dye-reversed replicates were performed. Regions of chromosomal amplification and deletion were determined based on circular binary segmentation provided by the Bioconductor DNA copy library.Total RNA was prepared using standard methods. RNA integrity was verified on an Agilent 2200 TapeStation  utilizing samples with RINs ≥8. Total RNA (100–200 ng) was used to prepare RNA-Seq libraries using the TruSeq RNA Sample Prep Kit following the protocol as described by the manufacturer . Libraries were clustered at a concentration to ensure at least 100 million reads per sample on the cBot as described by the manufacturer . Clustered RNA-seq libraries were paired-end sequenced using version 4 chemistry with 2 × 125 cycles on an Illumina HiSeq2500. Demultiplexing was performed utilizing bcl2fastq v2.17.1.14 to generate Fastq files for downstream analysis.2, 1 mM EGTA, 100 mM sodium fluoride, 10 mM sodium pyrophosphate, 1 mM sodium orthovanadate, 10% glycerol and protease/phosphatase inhibitors (Roche #05056489001/04906837001). Protein concentrations were determined by Bradford assay (BioRad) and concentrations were adjusted to 1 mg/ml. The samples were then mixed with 4× SDS sample buffer containing 0.2 M Tris–HCl (pH 8.0), 40% glycerol, and 8% SDS, boiled for 5 min, and stored at −80 °C until shipment to the RPPA Core Facility at MD Anderson for analysis.For RPPA analysis, cells were lysed in 100 μl RPPA lysis buffer containing 1% Triton X‐100, 50 mM HEPES, 150 mM NaCl, 1.5 mM MgCl53. Briefly, Agilent Sure Select XT reagents were used to prepare sequencing libraries. Hybrid capture was performed using Agilent Sure SelectXT Human All Exon V4 + UTRs, and 100 bp paired-end sequencing was performed on a HighSeq2000 achieving a median coverage of greater than 50-fold. Reads were aligned to the human reference genome GRCh37 using the Burrow-Wheeler Aligner. The data were processed further using the Genome Analysis Toolkit (GATK). For inclusion in the SLKBase, we cross-referenced all called SNVs with data in COSMIC and only report mutations that have occurred in COSMIC > 5 times. For the data from DepMap portal, we only report on the SLKBase mutations considered to be hot-spot mutations in COSMIC.Exome sequencing of SUM cell line DNA was performed essentially as described previously12. Briefly, virus pools expressing shRNA constructs were prepared according to the Cellecta Pooled Lentiviral shRNA Libraries User Manual protocol (www.cellecta.com). HEK 293 T cells were transfected with each of the three Cellecta library plasmid DNA pools (Human Modules 1–3) and the Cellecta Ready-to-Use Packaging Mix (Cat #CPCP-K2A). For each module, virus was titered and used to transduce 5 × 107 target cells at a MOI of ~0.3 in the presence of 5 µg/ml polybrene. Following transduction, cells were cultured for 3 days to allow expression of the resistance marker and non-transduced cells were eliminated from the culture by addition of the selective agent puromycin to the growth media. Three days after the addition of puromycin, cells were trypsinized and one half of the total population was harvested for genomic DNA preparation. The remaining cells were plated and grown for ~5–7 population doublings before harvesting for genomic DNA preparation. Genomic DNA was prepared by phenol:chloroform extraction according to the Cellecta Pooled Lentiviral shRNA Libraries User Manual protocol.The detailed methods that we used for our shRNA screens of the SUM lines have been reported previouslywww.usadellab.org) to trim read lengths to 18 nucleotides. Trimmed reads were deconvoluted using a Cellecta Barcode Analyzer and Deconvoluter software. Fold-depletion scores for each shRNA were calculated as the ratio of the read count at the reference time point versus the final time point.Barcode sequences were amplified from genomic DNA by two rounds of PCR as described previously. Amplified barcode sequences were run on a 3.5% agarose gel and purified using a QIAquick Gel Extraction Kit (Qiagen) according to the manufacturer’s instructions. Isolated barcode sequences were further purified using the PureLink Quick PCR Purification Kit (Invitrogen) according to the manufacturer’s instructions. For sequencing, purified barcodes were diluted to 0.75 ng/µl using buffer EB (Qiagen). Amplicons were clustered at 17 pM including 30% (v/v) PhiX to add sequence diversity. Single end (SE) clustering was performed on a Cbot according to the manufacturer’s protocol . A total of 36 cycles of SE sequencing were performed on an Illumina HiScanSQ. Custom primer GexSeqS  was added to the Illumina sequencing primers at 0.5 µM. Fastq files were generated using CASAVA 1.8.2 and processed using Trimmomatic software  would not be depleted, and their log-quantile scores would have a normal distribution. Based on this assumption, the median of the empirical distribution was used as an estimate of the mean of the null distribution. The estimate of the standard deviation of the null distribution was defined as the 97.5th quantile minus the 2.5th quantile, divided by 4. This was based on the knowledge that 95% of the data in a normally distributed variable falls between ± two standard deviations from the mean. Using this null distribution, all genes having log fold-depletion scores that were larger than the 95th percentile of the null distribution were identified as “hits”. Using this method, all genes that were hits in the screen had at least two, and usually more, shRNAs with depletion scores above the cut point.8, available at https://depmap.org/portal/download. We obtained metadata for each breast cancer cell line, such as the cell line’s Achilles ID, and then processed the data to prepare it for analysis with the tools available on the SLKBase. We performed median-centering normalization of the gene expression data. Specifically, to normalize the expression value of a gene within a cell line, we subtracted the expression value by the median expression of the gene among all breast cancer cell lines. Along with expression data, we obtained copy number amplification, mutation data (COSMIC hit count), and CRISPR scores of gene dependency effects that were calculated using the CERES method54. We followed DepMap by considering hits in the CRISPR screens to be those having CERES scores of ≤−0.5.We acquired data derived from other breast cancer cell lines from the Broad Institute’s Cancer Dependency Map project (DepMap)We developed a MySQL database to store the functional genomics data for the breast cancer cell lines. This database is hosted on Google App Engine. The database contains tables for cell lines, genes, and proteins. Gene tables contain basic information from Entrez  as well as a Boolean indicator if the gene is annotated as an oncogene by OncoKB. This allows us to unify gene essentiality information with a manual oncogene annotation. The database also contains linking tables between cell lines and genes that store the functional genomics data of a gene within a cell line (and likewise for proteomics data). For example, BCL2L1 has a linking table with each of the cell line tables; this linking table contains values for fold change, CNA, number of COSMIC mutations, etc.https://shiny.rstudio.com) to develop the tools available on the SLKBase website and the tools are deployed on RStudio’s shinyapps.io (https://shinyapps.io). Each of the apps uses the R package “RMySQL” to query the MySQL database for functional genomics data. Furthermore, the Pathway Engine tools utilize the “Pathview” package to map the genomics data to genes within KEGG pathways.We used the Shiny R package  and convert them to the format of an adjacency matrix suitable for input to NetworkX.The SLKBase web site was developed using WordPress tools and the HitMag style.Further information on research design is available in the Supplementary MaterialReporting Summary Checklist


Colorectal and glioblastoma cancer stem‐like cells (CSCs) are essential for translational research. Cell line authentication by short tandem repeat (STR) profiling ensures reproducibility of results in oncology research. This technique enables to identify mislabeling or cross‐contamination of cell lines. In our study, we provide a reference dataset for a panel of colorectal and glioblastoma CSCs that allows authentication. Each cell line was entered into the cell Line Integrated Molecular Authentication database 2.1 to be compared to the STR profiles of 4485 tumor cell lines. This article also provides clinical data of patients from whom CSCs arose and data on the parent tumor stage and mutations. STR profiles and information of our CSCs are also available in the Cellosaurus database (ExPASy) as identified by unique research resource identifier codes. Human cell lines obtained from cancer stem‐like cells represent an invaluable model for studying tumor properties. Cell line authentication by short tandem repeat (STR) profiling is an important tool to identify the potential mislabeling or cross‐contamination of cell lines. Here, the authors characterized 18 colorectal cancer stem‐like cell lines from 17 patients and 103 glioblastoma cancer stem‐like cell lines from 95 patients by STR profiling to create a reference dataset that allows the authentication of these cell lines and their identification through a unique research resource identifier. The results will help further ensure the reliability and reproducibility of research experiments. AMELXamelogenin in Xp22.1‐22.3AMELYamelogenin in Yp11.2CLASTRcellosaurus STR databaseCLIMACell Line Integrated Molecular Authentication databaseCSCcancer stem‐like cellCTSCcolorectal tumor stem‐like cellGSCglioblastoma stem‐like cellICLACInternational Cell Line Authentication CommitteeICLCCell Bank Interlab Cell Line CollectionMGMTO6‐methylguanine DNA methyltransferaseMSImicrosatellite instabilityPBMCperipheral blood mononuclear cellPCRpolymerase chain reactionSTRshort tandem repeat1Cancer research requires models that use patient‐derived cultured cells. These models allow to study tumor heterogeneity, in particular in the early stages of tumorigenesis. Isolation and in vitro cultivation of cancer stem‐like cells (CSCs), a small fraction of self‐renewing cells with stem‐like properties, provide a model to study the properties of these tumor initiating cells.https://web.expasy.org/cellosaurus/).STR profiling was carried out using standardized procedures for unambiguous authentication and identification of human cell lines according to the American National Standards Institute/American Type Culture Collection Standard ASN‐0002‐2011.The cell line profiles were challenged against public databases to exclude cross‐contamination with commercially available cell lines. Comparison of the cell line profiles against each other ruled out duplicates due to cross‐contamination. Duplicates were found only in those cell lines that derived from different regions of the same tumor.2Further method descriptions are included in 2.1CTSCs and GSCs were isolated from tumor samples through mechanical dissociation and cultured in a serum‐free medium supplemented with growth factors, as previously described.2.2All experiments were performed with mycoplasma‐free cells. Mycoplasma contamination in cell cultures was evaluated using MycoAlert Mycoplasma Detection Kit .2.3Single‐point mutations and small insertions/deletions in CTSCs were assessed by targeted DNA resequencing, focusing on 17 genes known to be frequently mutated in colon cancer, as previously described.Microsatellite instability (MSI) detection was performed using the Promega panel of mononucleotide MSI markers . The expression of four mismatch repair proteins, MLH1, MSH2, MSH6 and PMS2, was investigated in tumor tissues corresponding to CTSCs that showed MSI‐High.The expression of the stem cell marker CD133 and the epithelial antigen Ber‐EP4 in CTSCs was evaluated by flow cytometry, as previously described.2.4Tumor proliferation index was analyzed by immunohistochemistry on paraffin sections of glioblastoma samples using the avidin‐biotin‐peroxidase complex methods .2.5All human cell lines described have been authenticated by STR profiling within the last 3 years. Genomic DNA was isolated from cell pellets using the DNeasy Blood & Tissue Kit  and treated with RNase, according to the manufacturer's instructions. Yield was measured with NanoDrop 1000 spectrophotometer . One nanogram of DNA of each sample was used for the STR analysis. Samples were amplified and electrophoretically separated on an ABI Prism 3100 Genetic Analyzer (Applied Biosystems).STR profiles were analyzed by the GeneMapper ID software (Applied Biosystems), Version 3.2. The results showed highly reproducible cell line‐specific numeric patterns. The assay was performed by the Cell Bank Interlab Cell Line Collection (ICLC) of the Biological Resource Centre, IRCCS Ospedale Policlinico San Martino of Genoa, in collaboration with the Department of Legal and Forensic Medicine of Genoa University. Comparison of STR profiles using the CLIMA database was performed using the identification feature in CLIMA 2.1 version of the database, as previously described.33.1ACVR1B, AMER1, APC, BRAF, CTNNB1, FBXW7, KIAA1804, KRAS, MAP2K4, NRAS, PIK3CA, PTEN, SMAD2, SMAD4, SOX9, TCF7L2 and TP53 . Tumor site and disease stage  of CTSCs are shown in Table 53 Table .Analysis of MSI status using mononucleotide MSI markers showed that 6 CTSC lines had MSI‐High , one line had MSI‐Low (CTSC#417) and the other lines were microsatellite stable . In accoCTSCs were analyzed for the expression of CD133 and epithelial antigen Ber‐EP4 . CD133 iMGMT gene methylation status and proliferation index (Ki67) of parent tumors are shown in Table MGMT gene promoter status was methylated in 46 tumors (49%), unmethylated in 44 tumors (46%) and not available in 5 tumors (5%).We characterized 103 GSCs from 95 glioblastoma patients . Tumor location, disease stage (primary/recurrent tumor), For GSCs, the MSI status was not analyzed since its frequency, as determined through the amplification of the monucleotide loci, is rare in glioblastoma tumors. Single loci MSI are observed in a low percentage of glioblastoma samples and the presence of high MSI is not a typical feature of this tumor.GSCs were also analyzed for the expression of the markers CD133 and Sox2 . Express3.2Eighteen CTSCs and 103 GSCs were genetically identified using STR profiling with two different kits using 10 or 16 different loci . To protAmelogenin marker was used for gender determination.Failure in amelogenin sex gene detection is rare in healthy individualsAmong the GSCs that arose from 66 male individuals, 16 cell lines showed only AMELX . Therefore, about 24% of the male‐derived cell line profiles seem to have lost AMELY. It has been reported that about 40% to 45% of cell lines purportedly derived from males lacked the AMELY allele.3.3The analysis of STR profiles of CTSCs and GSCs by CLIMAIn some cell lines, PBMCs and the primary tumor tissues (T) were also analyzed for STR profiles. Results were as follows, GSC#298 and #298 PBMCs (100% of similarity); GSC#309s, #309T and #309 PBMCs (100% of similarity); 314T and 314 PBMCs (100%); GSC#318 and #318T (100%). All the other cell lines have unique profiles, that do not match with other profiles for a percentage higher than 90%. All unique profiles have been confirmed in CLASTR.4Human cell lines obtained from CSCs represent an invaluable model for studying the properties of tumors.In the present study, we detected MSI‐H in 7 out 18 CTSC cell lines (38%) that is higher than expected in CRC. There are two possible explanations for this result. The first is that 13 72%) tumor samples, which the CTSCs were isolated from, came from right‐sided colon cancers that harbor the MSI‐H phenotype most often.% tumor swww.ICLAC.org), a voluntary, independent scientific committee, established in 2012. ICLAC produces important guidelines, such as “Guide to Human Cell Line Authentication” and “Obtaining Cell Lines from Reliable Sources.” ICLAC was established after the publication of a consensus Standard for human cell line authentication by STR profiling.https://f1000research.com/articles/4-134/v2) determined the use of an unique research resource identifier to flag each established cell line for searches and data analysis.Despite the success of using cell lines as models to advance cancer research, misidentification of cell lines is a widespread problem.Using STR profiling, we generated for each CSC line a unique molecular identity pattern. STR profiles from CTSCs and GSCs were compared both with cell line profiles included in CLIMA 2.1 databaseThe cell lines used in our study will be available to researchers through Material Transfer Agreement (MTA).The authors declared no potential conflicts of interest.Glioblastoma tissue samples were harvested from patients undergoing craniotomy at the Institute of Neurosurgery, Università Cattolica del Sacro Cuore (UCSC), Rome, Italy. All the patients provided written informed consent according to the research proposals approved by the Ethical Committee of UCSC. Fresh human colorectal cancer tissues were obtained in accordance with the standards of the ethics committee on human experimentation of the Istituto Superiore di Sanità (authorization no. CE5ISS 09/282). All the patients provided written informed consent. Cell lines obtained from tumor stem‐like cells were de‐identified to protect patient health information.Appendix S1: Supporting InformationClick here for additional data file.


Neocryptodiscus papillaris based on moderate cytotoxic activity of the extract in COLO205, KM12 and MCF7 cancer cells. While the major prenylated furanocoumarin derivatives and osthol isolated from the dichloromethane extract were responsible for the activity in the colon and breast cancer cell lines, the 4′-acylated osthol derivatives including a novel coumarino-alkaloid; neopapillarine) demonstrated selective cytotoxic activity in A498 and UO31 renal cancer cell lines.Several simple and prenylated coumarin derivatives were isolated from the dichloromethane extract of the root of  Neocryptodiscus ; thrine (4) .Aspergillus species, also contain a coumarin nucleus as part of their polycylic heterocyclic structure +  indicating a molecular formula of C15H16O4 for 2 which that is in agreement with the hydoxylated isoprenyl side chain added to the osthol structure. Although this compound has previously been reported as a rat metabolite of osthol  strongly suggested that the C-4′ hydroxymethylene group was esterified with an acetyl group in 3. In addition, the position of the acetoxy group was corroborated by the 2D NOESY spectrum as C-4′ and furthermore, a sodium adduct molecular peak observed at m/z 325.1039 [M + Na]+  in the HRESIMS spectrum of 3 confirmed the structure of 3 as 4′-acetoxyosthol.The thol (3)  clearly 1H-NMR spectrum of neopapillarine (4)  confirming a molecular formula of C21H19NO5 for 4 which that is in agreement with a 4′-nicotinoyloxyosthol structure for compound 4.The rine (4)  showed sd esters , thus, tyl group  as with N. papillaris was tested in A498 and UO31 renal cancer cell lines  or nicotinoyloxy  group displayed a moderate inhibitory activity against the UO31 cell line. Furthermore neopapillarine (4), the 4′-nicotinoyloxy derivative of osthol, exhibited better inhibitory activity in the A498 renal cancer cell line in comparison with the 4′-senecioyloxy derivative of osthol (5).The cytotoxic activity of the coumarins isolated from ll lines . While mN. papillaris, a rare and endemic Apiaceae plant, yielded several coumarin derivatives with cytotoxic activity including a novel coumarino-alkaloid compound; named neopapillarine (4). The prenylated major coumarins of the root extract were osthol (1), isoimperatorin (8) and oxypeucedanin (9). Osthol (1) showed 20 and 24 μM IC50 values in COLO205 colon cancer and MCF7 breast cancer cell lines, respectively. Oxypeucedanin (9) displayed 30 and 31 μM IC50 values in the COLO205 and KM12 colon cancer cell lines, but much weaker IC50 values  against the MCF7 mammalian and A498 and UO31 renal cancer cell lines, respectively. Isoimperatorin (8) showed an IC50 value of 14 μM in the COLO205 cancer cell line but a weaker inhibitory activity  against the KM12 and MCF7 cancer cell lines, respectively. In contrast, only neopapillarine (4) and 4′-senecioyloxyosthol (5) showed inhibitory activity with an IC50 of 20 μM in the UO31 renal cancer cell line. While neopapillarine (4) displayed a moderate inhibitory activity with an IC50 of 67 μM in the A498 cell line, 4′-senecioyloxyosthol (5) showed relatively weak activity in the A498 cell line, this difference may be due to the nitrogen bearing ester group of neopapillarine (4).The Investigation of the dichloromethane extract of the root of 14) was the only other known nicotinic acid ester of a prenylated coumarin isolated from a plant species of the Rutaceae family, i.e., Murraya paniculata (L.) Jack, [15) [16)) [17)) [18), conyhydrine) [19)) [20)  [21) [To date, isomurralonginol nicotinate (.) Jack, . Interesck, [15) , dimeric5) [16)) ,31 and n)) [17)) ,33, dimehydrine) . Neverthe) [19))  and two anilate)  and a fute) [21)  have bee1H and 150 MHz for 13C and equipped with a 3 mm cryogenically cooled probe. HRESIMS data were acquired on an Agilent 6530 Accurate Mass Q-TOF instrument . Initial purification of the dichloromethane extract was carried out on a Sephadex LH-20  column. Further purification of column fractions was performed using silica gel F254 PLC plates (1 mm thickness) .UV spectra were recorded on the Shimadzu UV-Vis Spectrophotometer, UV-1700 . IR spectra (neat) were recorded on the Perkin-Elmer FT-IR Spectrometer, SPECTRUM 2000 . NMR spectra were acquired on a Bruker Avance III spectrometer  operating at 600 MHz for N. papillaris were collected from Aşağı Dilimli village, near Viranşehir, Şanlıurfa in June 2013 and identified by Prof. A. Duran. A voucher specimen (A. Duran 7780) was deposited in the Herbarium of Selçuk University, Faculty of Sciences, Department of Biology. Due to the endangered rare plant species status of Neocryptodiscus papillaris, only a portion of the root was taken from the plant  of the plant material, coarsely powdered root (200 g) and fruits (100 g) of N. papillaris were extracted sequentially with dichloromethane (4 L for the root and 2 L for the fruits) and methanol (4 L for the root and 2 L for the fruits) at room temperature and concentrated, in vacuo, to dryness . The dichloromethane root extract (4 g) was separated using a Sephadex LH-20 column (4.5 × 100 cm) packed in a hexane/dichloromethane/methanol (14:9:1) mixture followed by prep. TLC  for final purification of compounds. The known compounds isolated from the dichloromethane extract were osthol , 4′-senecioyloxyosthol , psoralen , bergapten , isoimperatorin , oxypeucedanin , oxypeucedanin hydrate , pranferol , scopoletin  and scoparone .Upon complete drying . White amorphous powder; IR (NaCl) νmax: 3450, 2918, 2850, 1730, 1607, 1498, 1403, 1281, 1252, 1120, 1091, 832 cm−1; UV (MeOH) λmax (log ε): 321 (3.80), 257 (3.39), 248 (sh) (3.40), 214 (3.94) nm; 1H and 13C-NMR .4′-Hydroxyosthol . White amorphous powder; IR (NaCl) νmax: 2933, 2850, 1742, 1730, 1609, 1498, 1436, 1403, 1367, 1281, 1251, 1118, 1090, 1028, 833 cm−1; UV (MeOH) λmax (log ε): 322 (3.91), 257 (3.51), 248 (3.50), 210 (3.99) nm; 1h and 13C-NMR .4′-Acetoxyosthol . White amorphous powder; IR (NaCl) νmax: 2919, 2850, 1723, 1608, 1498, 1436, 1281, 1251, 1161, 1117, 1089, 1024, 833, 742, 703 cm−1; UV (MeOH) λmax (log ε): 321 (3.23), 271 (sh) (2.98), 258 (3.11), 190 (sh) (3.63) nm; 1h and 13C-NMR .Neopapillarine  were diluted 1:25 with medium (192 μL) and then subjected to five 2:1 serial dilutions (100 μL each) on a 96-well plate. Duplicate 40 μL aliquots of each sample concentration were then transferred to a 384-well “dilution plate”, which could accommodate the duplicate samples from two 96-well plates. A 5 μL portion of each solution in the dilution plate was transferred to the cell cultures in the 384-well assay plates to give a final volume of 50 μL and a DMSO concentration of 0.4%. Control wells included 8 wells with the positive control sanguinarine chloride at 20 µM, as well as DMSO only controls and no cell controls. The Z’ factors for the individual plates were calculated and were >0.5 in all cases. The cells were incubated for 48 h at 37 °C in the presence of the test samples and then treated with the tetrazolium salt XTT . Viable cells reduced XTT to a colored formazan product, and after an additional 4 h incubation period the amount of formazan produced was quantified by absorption at 450 nm, using a 650 nm reference. Plates were read on a PerkinElmer EnVision (model # 2104) reader.The assay used for this study was a two-day, two cell line XTT bioassay , an in v