LA-HSA) resulted in substantial particle uptake with low cytotoxicity. In contrast, uptake of lauric acid-coated nanoparticles (SPIONLA) was substantially increased but accompanied by higher toxicity. The presence of an external magnetic field significantly increased cellular uptake of both particles, although cytotoxicity was not significantly increased in any of the cell lines. SPIONs coated with lauric acid and/or human serum albumin show different patterns of uptake and toxicity in response to an external magnetic field. Consequently, the results indicate the potential use of SPIONs as vehicles for MDT in head and neck cancer.Superparamagnetic iron oxide nanoparticles (SPIONs) feature distinct magnetic properties that make them useful and effective tools for various diagnostic, therapeutic and theranostic applications. In particular, their use in magnetic drug targeting (MDT) promises to be an effective approach for the treatment of various diseases such as cancer. At the cellular level, SPION uptake, along with SPION-mediated toxicity, represents the most important prerequisite for successful application. Thus, the present study determines SPION uptake, toxicity and biocompatibility in human head and neck tumor cell lines of the tongue, pharynx and salivary gland. Using magnetic susceptibility measurements, microscopy, atomic emission spectroscopy, flow cytometry, and plasma coagulation, we analyzed the magnetic properties, cellular uptake and biocompatibility of two different SPION types in the presence and absence of external magnetic fields. Incubation of cells with lauric acid and human serum albumin-coated nanoparticles (SPION LA) and SPIONs coated with lauric acid and human serum albumin (SPIONLA-HSA) on various head and neck cancer cell lines. Accordingly, the primary objective of the present work was to investigate the effects of both SPION systems on viability, cell proliferation and uptake in head and neck tumor cell lines of squamous cell carcinoma of the pharynx, tongue and parotid gland. In addition, the influence of magnetically enhanced SPION accumulation, as a prerequisite for their suitability as potential vehicles for MDT, was investigated by culturing the cells either without or on magnetic plates. Furthermore, biocompatibility with human blood representing the requirement for future in vivo applications served as secondary objective and was analyzed using coagulation assays with human blood plasma.Squamous cell carcinoma of the head and neck is the sixth most common malignant tumor in the world, with 500,000 new cases every year . In part2⋅4H2O), iron (III) chloride hexahydrate (FeCl3⋅6H2O) and Sartorius ultrafiltration tubes Vivaspin 20, PES with a molecular weight cut-off (MWCO) of 100 kDa were purchased from VWR, Darmstadt, Germany. Iron reference standards were bought from Bernd Kraft GmbH, Duisburg, Germany. Glass coverslips, sterile Rotilabo® syringe filters with cellulose mixed ester membranes, dialysis bags , nitric acid, DMSO, trypsin, PBS, ethanol, disodium hydrogen phosphate, gelatin and formalin were supplied by Roth, Karlsruhe, Germany. Propidium iodide (PI), Triton X-100, lauric acid and Ribonuclease A were purchased from Sigma-Aldrich, St. Louis, MO, USA. Anexin V-FITC (AxV-FITC), Hoechst 33342, Alexa Fluor 488 Phalloidin and 1,1′,3,3,3′,3′-hexamethylindodicarbocyanine iodide (DiIC1(5)) were purchased from Life Technologies, Darmstadt, Germany. Ringer’s solution were purchased from Fresenius Kabi AG, Bad Homburg, Germany. Fetal calf serum (FCS) superior, were purchased from Thermo Fisher Scientific, Waltham, MA, USA. EMEM, DMEM: F-12 and McCoy’s 5A Medium were purchased from ATCC®, Manassas, VA, USA. Hydrocortisone was purchased from Pfizer, New York City, NY, USA. Ferucarbotran (Resovist®) were purchased from I’rom Group, Tokyo, Japan and Ferumoxytol (Rienso®) was purchased from Takeda Pharmaceutical Company Limited, Osaka, Japan. Water used in all experiments was of ultrapure quality produced by the Milli-Q® system from Merck, Darmstadt, Germany. Other materials and equipment used in this study includes: Human serum albumin (HSA) solution ; Agilent 4200 microwave plasma-atomic emission spectrometer (MP-AES) ; Malvern Zetasizer ; MS2G Susceptometer, Bartington® Instruments, Oxon, UK); aPTT Kit and 25 mM CaCl2 solution ; MC4 plus plasma coagulometer ; Magnetic plates ; S-15-08-N magnets ; FaDu (ATCC® HTB-43™), SCC-9 (ATCC® CRL-1629™), A-253 (ATCC® HTB-41™) and Detroit 562 (ATCC® CCL-138™) cell lines ; Gallios cytofluorometer and Kaluza software version 2.0 ; Incucyte® ; xCELLigence ; Mounting Medium ; Zeiss Axio Observer. Z1 fluorescent microscope ; Nanolive Fluo-3D Cell Explorer® and Steve software v.1.6.3496® , Thermal mixer , KrosFlo® Tangential flow ultrafiltration equipment .Iron (II) chloride tetrahydrate  was performed according to a protocol of Zaloga et al., 2016 . Fu. Fu25]. y images . This beLA-HSA or SPIONLA, either in presence (+) or in absence (−) of a magnetic force. We then analyzed the cellular iron content by microwave plasma-atomic emission spectrometry (MP-AES) as well as compared the data with the results of holotomography and flow cytometry  and atomic emission spectroscopy (AES) . We therytometry . WithoutLA and SPIONLA-HSA on viability, necrosis, apoptosis, mitochondrial membrane integrity, DNA fragmentation, and cell cycle  measurements using flow cytometry after DiIC1(5) and Hoechst staining  staining . At 50 and 100 µgFe/mL, SPIONLA-HSA had no negative effect on cell proliferation. At the lowest concentration, cell growth was even enhanced, indicating a known effect of SPION-induced enhanced cell growth at lower concentrations that is reduced or abolished at increasing SPION concentrations due to cytotoxic effects [The lower cytotoxicity of SPIONstaining . All celstaining . The mea imaging . The eff effects .LA and SPIONLA-HSA were added in ascending concentrations to fresh human citrate-stabilized plasma. After addition of activated partial thromboplastin time (aPPT) solution, coagulation was started with CaCl2 solution and clotting time was automatically monitored  increase in cellular particles. Interestingly, after incubation with SPIONLA at the same concentration, the increase for iron detected by MP-AES was very similar (5.9-fold to 12.9-fold), indicating the presence of a similar magnetic susceptibility for SPIONLA and SPIONLA-HSA. Thus, the presence of a magnetic field allows a 5.9-fold to 13.8-fold increase in particle uptake, which is similar for all cell lines and particles studied. However, there are significant cell type-dependent differences in absolute SPION uptake. While cells derived from tongue carcinoma (SCC-9) incorporated the most particles, closely followed by salivary gland carcinoma cells (A-253), the efficiency of cells derived from metastases of the hypopharynx (FaDu) and pharynx (Detroit 562) to take up particles was significantly lower. These cell-based differences were enhanced after application of a magnetic field. Thus, the observed variations in SPION uptake on a cellular level are not only due to the physicochemical properties of the coating but also to the different characteristics of head and neck cancer cells. Interestingly, despite an enhanced uptake, application of an external magnetic field revealed only a slight effect on cell viability in FaDu cells, whereas there was no obvious impairment of cell viability in Detroit 562, A-253, or even SCC-9 cells, which exhibited the highest SPION loadings. This is a surprising result, as increased SPION content can be expected to increase cytotoxicity. However, the magnetic field could induce the formation of larger aggregates that are only deposited on the cell membrane and do not enter the cell by active uptake mechanisms. Previously, a study addressing the magnetic settling of SPIONs demonstrated that magnetic fields lead to particle aggregation and a faster settling of magnetic nanoparticles [LA -incubated cells). Flow cytometric quantification via SSc also underrepresents the particle amount because detached particle aggregates were gated out and apoptotic and necrotic cells, which may contain high particles quantities, were excluded from SSc determination to avoid including the increased SSc of dying cells , showed a comparatively higher particle uptake of 3.0 pg/cell. However, comparing the results is limited as breast cancer cells were not exposed to a magnetic field and incubated with the highest SPION concentration of 75 μgFe/mL. The results presented strongly suggest that despite the heterogeneity of cellular origin, the feasibility of MDT may be expanded to various entities beyond breast and head and neck cancer cell lines. However, SPIONLA exhibited substantial toxicity and decreased biocompatibility compared with SPIONLA-HSA (LA-HSA is to be considered as the more appropriate particle to be used for MDT in head and neck cancer.Our study is consistent with Poller et al., who demonstrated that breast cancer cells incubated with SPIONtoxicity . Notablyr values . The detONLA-HSA . Based oThe present work provides new results on the interaction of SPIONs with head and neck cancer cell lines. The examined cell lines demonstrated a distinct ability to take up superparamagnetic iron oxide nanoparticles. Applying an external magnetic field does not impair cell growth and viability. Accordingly, the results indicate that head and neck cancers may serve as potential candidates for magnetic drug targeting using SPIONs.


For transporter substrates, differences between the two cell lines were less pronounced but the drugs displayed different inhibition potencies when evaluated by the two methods. Our study illustrates the inherent limitations when comparing transporter inhibition data from different laboratories and stresses the importance of including appropriate control experiments with reference compounds when investigating new drugs of interest.High-affinity monoamine transporters are targets for prescribed medications and stimulant drugs of abuse. Therefore, assessing monoamine transporter activity for candidate medications and newly-emerging drugs of abuse provides essential information for industry, academia, and public health. Radiotracer binding and uptake inhibition are the gold standard assays for determining drug–transporter interaction profiles. The combined results from such assays yield a unique biochemical fingerprint for each compound. Over time, different assay methods have been developed to assess transporter activity, and the comparability of data across various assay platforms remains largely unclear. Here, we compare the effects of six well-established stimulants in two different cell-based uptake inhibition assays, one method using adherent cells and the other using suspended cells. Furthermore, we compare the data from transfected cell lines derived from different laboratories and data reported from rat synaptosomes. For transporter inhibitors, IC These transporters contain 12 transmembrane helical domains with intracellular amino and carboxy termini (High-affinity monoamine transporters (MATs) for serotonin (5-hydroxytryptamine [5-HT]), dopamine, and norepinephrine  are transmembrane proteins of the solute carrier 6 family that transport substrate molecules across the plasma membrane bilayer, using ion gradients , and the transporter substrates d-amphetamine, 3,4-methylenedioxymethamphetamine (MDMA), fenfluramine, and 4-methylmethcathinone (mephedrone) were chosen as drugs of interest. With this study, we aim to better understand how different assay variables influence the results obtained, and examine whether the identification of certain trends in the data might facilitate a better comparison of results.Currently, the two main assay systems used to assess uptake inhibition characteristics of new compounds are transporter-transfected human embryonic kidney (HEK) 293 cells and synaptosomes derived from rat brain. Transfected cells bear the advantage of expressing pure populations of a single human transporter of interest, whereas synaptosomes consist of sealed nerve endings which possess all of the protein machinery for transmitter synthesis, release, metabolism, and uptake. The specific experimental methods employed by different research laboratories can vary considerably, leading to high variability in transporter inhibition potency values, as reported in the scientific literature for transfected cells  and for Transporter inhibition was assessed in two different laboratories, one performing experiments on adherent cells (referred to as “method 1”) and the other performing experiments on cells in suspension (referred to as “method 2”). Each of the laboratories independently established stable cell lines expressing human isoforms of SERT, DAT or NET using their stock of HEK 293 cells. For reasons of simplicity, human MAT expressing cells established in Vienna and Basel, will henceforth be referred to as “cell line 1” and “cell line 2,” respectively. Both cell lines have been previously used to assess the pharmacological profiles of stimulants in multiple studies Table 1.d-amphetamine sulfate (Sigma Aldrich) and d-amphetamine hydrochloride (Lipomed), fenfluramine hydrochloride (Lipomed), MDMA hydrochloride (Lipomed), and mephedrone hydrochloride (Lipomed).Due to the limited availability of certain drugs in different countries, the contributing laboratories obtained some of the test drugs from different sources. The following test drugs were included: cocaine hydrochloride , MDPV hydrochloride (Lipomed), 2 ∙ 2H2O, 2 mM MgCl2 ∙ 6H2O, pH 7.3) and Krebs-Ringer Bicarbonate buffer . KHB used for binding experiments at DAT additionally contained 10 µM ZnCl2. The lysis buffer used in method 2 was composed of 0.05 M Tris–HCl, 50 mM NaCl, 5 mM EDTA, and 1% NP-40 in purified water. Radiotracers used in method 1 and uptake saturation experiments were purchased from American Radiolabeled Chemicals, Saint Louis, MO, USA ([3H]-1-methyl-4-phenylpyridinium ([3H]-MPP+), 80 Ci/mmol) or from Perkin Elmer, Boston, MA, USA . Radiotracers used in method 2 were purchased from Perkin Elmer,  or Anawa, Zürich, Switzerland . [3H]-CFT , 82.9 Ci/mmol, used in binding experiments was obtained from Perkin Elmer Boston, MA, USA.Buffers used for the uptake inhibition, uptake saturation, and radioligand binding experiments were as follows: Krebs HEPES buffer  and subsequently grown in 10-cm tissue culture dishes. Upon reaching approximately 80% confluence, the cells were washed once with 6 ml of phosphate-buffered saline  and detached with 1 ml of trypsin-EDTA (Sigma-Aldrich). The cell trypsinization was stopped after 3 min by adding 9 ml of pre-warmed DMEM. The cells were seeded onto poly-D-lysine-coated  96-well culture plates at the density of 0.2 × 106 cells/ml 24 h prior to the experiment and grown as monolayers.HEK 293 cells used to create cell lines stably expressing MAT proteins in Vienna, Austria were purchased from LGC Standards GmbH, Wesel, Germany. Human isoforms of DAT and SERT were transfected using jetPRIME transfection reagent  according to manufacturer's instructions. The human isoform of NET and rat isoforms of DAT, NET, and SERT were transfected using the CaPOlsewhere . Cells w4 method as previously described  stably transfected with the human NET, DAT, or SERT using the CaPOescribed  were crei). To initiate uptake, the preincubation buffer was exchanged for a buffer containing various concentrations of the test drugs, vehicle control, or monoamine-specific inhibitors with the addition of 20 nM [3H]-MPP+, 200 nM [3H]-dopamine, or 100 nM [3H]-5-HT. The uptake was carried out at room temperature for 1 min (for DAT and SERT) or 3 min (for NET) and the reaction was subsequently stopped by rapid removal of the uptake buffer and washout with ice-cold KHB. Thereafter, 300 µl of 1% SDS was added to the wells and cell lysates were transferred into 2 ml of scintillation cocktail. The amount of accumulated [3H]-substrate was determined by liquid scintillation counting on a Packard Tri-Carb 2300 TR liquid scintillation analyzer.Assays were performed as previously described by 6 cells/ml. For [3H]-dopamine uptake experiments, the buffer was additionally supplemented with 0.2 mg/ml ascorbic acid. The cell suspension (100 μl per well) was transferred into a round bottom 96-well polypropylene storage plate  and incubated in triplicate with 25 μl of KRB containing the test drugs in the range of 1 nM to 900 μM final assay concentration , vehicle control, or monoamine-specific inhibitors  for 10 min at 450 rpm and ambient temperature on a rotary shaker. To initiate uptake transport, 50 μl of [3H]-norepinephrine, [3H]-dopamine or [3H]-5-HT dissolved in KRB were added at a final concentration of 5 nM for additional 10 min. Thereafter, 100 μl of the cell suspension was transferred into 0.5-ml microtubes  that contained 50 μl of 3 M KOH covered with 200 μl silicon oil . The tubes were centrifuged with a Mikro 220 centrifuge  for 3 min at 16,550 × g to transport the cells through the silicone oil phase into the KOH. The tubes were frozen in liquid nitrogen and the cell pellets were cut from the bottom of the tubes and transferred into 6 ml scintillation vials (Perkin-Elmer) that contained 0.5 ml lysis buffer. The samples were shaken for 1 h at 700 rpm before 3 ml scintillation fluid  was added. After 1-h equilibration time, monoamine uptake was quantified by liquid scintillation counting on a Packard Tri-Carb 1900 TR liquid scintillation analyzer. Nonspecific uptake in the presence of selective inhibitors was subtracted from the total counts.Upon 70%–90% confluence, HEK 293 cells cell lines stably expressing NET, DAT, or SERT were washed once with 10 ml of PBS (Gibco) and detached with 2 ml (SERT) or 4 ml (NET and DAT) of 0.05% Trypsin-EDTA (Gibco). After 2 min, the trypsinization was stopped with 10 ml (SERT) or 8 ml (NET and DAT) of pre-warmed culture medium, the cell suspension was transferred into a 15-ml polypropylene tube (Techno Plastic Products), and the cells were then centrifuged at 1100 revolutions per minute (rpm) for 2 min at ambient temperature with a Heraeus Multifuge X1R . Thereafter, the medium was removed, and the cells were washed once with KRB, centrifuged again, and then resuspended in KRB at a density of 3 × 10Cell Culture section, washed once with 100 µl of room temperature KHB and incubated for 1 min (for DAT and SERT) and 3 min (for NET) at room temperature in 50 µl of the KHB containing various concentrations of the [3H]-substrate. The dilution row of [3H]-substrate was created by mixing various concentrations of non-tritiated substrates with a constant amount of [3H]-substrates . 100% of uptake was obtained in the presence of [3H]-substrate only and non-specific uptake was determined in the presence of monoamine-specific inhibitors.The cells were prepared as described for adherent cells in the 3H]-CFT. Saturation binding was performed in KHB containing various concentrations of non-tritiated β-CFT and 10 nM [3H]-CFT. After the incubation, cells were washed twice with 100 µl of ice-cold KHB and lysed with 1% SDS. The amount of released tritiated substrate was quantified by liquid scintillation counting on a Packard Tri-Carb 2300 TR liquid scintillation analyzer.For the measurement of inhibitor binding, cells were washed once with 100 µl of room temperature KHB and incubated for 30 min at room temperature in 50 µl of KHB containing various concentrations of inhibitors and 10 nM -CFT binding by cocaine at SERT (8-fold) followed by inhibition by d-amphetamine at NET and MDPV at SERT (3-fold difference). Approximately half of the IC50 values did not differ among two cell lines and the rest differed by a factor of 2 or less  lower than Km and Vmax values of human SERT expressing cell line 1  lower when compared to the two human NET-expressing cell lines. Values for rat DAT were 2-fold higher than in both cell lines expressing human DAT. The binding site density (Bmax) for β-CFT binding was similar for DAT and NET among all three cell lines . Notably, the IC50 values for the binding inhibition by cocaine at all rat transporter-transfected cell lines showed typically less than a 2-fold difference when compared to the IC50 values of the uptake inhibition in rat synaptosomes and 2.5- and 4-fold difference for SERT and NET cell lines, respectively, when MDPV was used  and should not affect the outcome  dopaminergic activity over serotonergic activity  of these catecholamine transporters, whereas the sequence homology with SERT binding site is much lower   .The comparison of inhibition potencies of substances at cell lines expressing human or rat MATs 5 result50 values for the inhibition of [3H]-CFT binding were more consistent compared to the IC50 values for the uptake inhibition. Similar to uptake inhibition, the most notable difference in the IC50 values for binding inhibition was observed for SERT cell lines . The next highest variation between two cell lines was observed in the case of norepinephrine uptake inhibition induced by d-amphetamine, MDMA, and mephedrone, even though there was no difference in Kd and Bmax values for β-CFT binding. There was no difference in β-CFT binding inhibition among all substances when two different DAT cell lines were assayed. This is in agreement with no differences in Kd and Bmax values for the two DAT cell lines.The ICll lines Table 3.When the two human MATs expressing cell lines were compared to rat MATs expressing cell lines, transporter density varied for SERT and DAT Table 4.A puzzling finding of this study was the fact that when using the method with cells in suspension, only one of the two stably DAT-transfected cell lines could be used, whereas with the other cell line no sigmoidal uptake curve was observed for any of the substances. The same phenomenon has recently been observed for catechol metabolites of MDMA and 3,4-methylenedioxymethcathinone (methylone) . A possiin situ versus rat SERT expressed in HEK cells. Similarly, all stimulants exhibited higher potencies at synaptosomal rat DAT and rat NET compared to the respective recombinant transporters expressed in HEK cells. An exception to this was mephedrone, which exhibited higher potency at recombinant rat NET compared to synaptosomal rat NET. The observed discrepancies might be related to fundamental differences between synaptosomes and intact cells. Synaptosomes are tissue homogenates containing an enriched preparation of sealed nerve endings, which may facilitate a higher probability for drug–transporter interactions. Furthermore, transporter proteins expressed in synaptosomes are surrounded by neuron-specific protein partners that are not present in HEK cells. Finally, procedural differences between the synaptosome experiments vs. cell-based experiments could be a factor. For example, the incubation times for MAT uptake assays in adherent cells are 1–3 min whereas incubation times for synaptosomes are 5–30 min , the National Institute on Drug Abuse, National Institutes of Health (grant No. DA 00523 to MB), the Austrian Science Fund , the Vienna Science and Technology Fund , and the Swiss National Science Foundation .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The reviewer SB declared a past co-authorship with several of the authors ML, MB, HS, and DL to the handling editor.


Immunoblot analysis showed elevated levels of γ-H2AX and cleaved PARP proteins upon drug combination treatment, indicating increased levels of DNA damage (double-strand break events: DSBs) and apoptosis induction, respectively. We propose that the mechanism of synergy involves SAHA relaxing condensed chromatin, resulting in higher levels of G4 formation. In turn, CM03 can stabilise a greater number of G4s, leading to the downregulation of more G4-containing genes as well as a higher incidence of DSBs due to torsional strain on DNA and chromatin structure.The stabilisation of G-quadruplexes (G4s) by small-molecule compounds is an effective approach for causing cell growth arrest, followed by cell death. Some of these compounds are currently being developed for the treatment of human cancers. We have previously developed a substituted naphthalene diimide G4-binding molecule (CM03) with selective potency for pancreatic cancer cells, including gemcitabine-resistant cells. We report here that CM03 and the histone deacetylase (HDAC) inhibitor SAHA (suberanilohydroxamic acid) have synergistic effects at concentrations close to and below their individual GI Pancreatic cancer is the fourth leading cause of cancer-related deaths in the USA and is expected to be the second or the third in developed and high-income countries in the next ten years . The mosWe have recently developed a class of potential drugs for PDAC that are based on the naphthalene diimide (ND) chemotype ,4. The lG-quadruplex (G4) sequences comprising short G-rich repetitive elements can be found in promoter and 5′ and 3′ untranslated regions of many genes (being over-represented in cancer-related genes) and in telomeres ,14,15,16A number of drugs that cause epigenetic remodeling such as methylation inhibitors and histone deacetylase (HDAC) inhibitors are in clinical trials as anticancer drugs ,30,31, aInhibition of chromatin condensation by the HDAC inhibitor SAHA, thus exposing potential G4 sites within a gene, for example in regulatory or non-coding regions.G4-binding compounds can maintain the unwinding of duplex regions of G4-containing genes within active chromatin by stabilizing G4 structures within these genes.This study has taken as a testable hypothesis the concept that the two effects can act in conjunction and thus synergize each other:We have used the clinically approved HDAC drug SAHA to investigate potential synergistic effects with a potent G4 binding compound, the naphthalene diimide compound CM03, which was developed in these laboratories .50 values of at least one of them, cell growth inhibition of 50% or more is produced. 50 values for the various compounds and drugs used in this study. Synergy between two compounds means that when administered together at lower concentrations than the GI50 value for each individual drug. Less pronounced effects are apparent in the GR3-PANC-1 cell line.Effects in the two gemcitabine-resistant PDAC cell lines are shown in The drug combination between SAHA and CM03 also shows a synergistic induction of apoptosis and DNA damage in the wild-type MIA PaCa-2 and PANC-1 cell lines, based on immunoblotting analysis of markers for apoptosis and DNA damage . Higher Previous studies on CM03 and gemcitabine using RNA-seq methods to map the transcriptome following drug treatment in PDAC cells have identified those genes downregulated by these drugs ,9,10. FiThe cell-based study reported here has demonstrated that a G-quadruplex ligand (CM03), in combination with the HDAC inhibitor SAHA, can produce >50% synergistic cell growth inhibition in the pancreatic cancer cell lines MIA PaCa-2 and PANC-1, as well as in these derived gemcitabine resistant lines. The study has identified effective two-drug combinations that show these levels of growth inhibition at concentrations below their individual GI50 values. Two other HDAC inhibitors, panobinostat and romidepsin, also show a synergistic effect in combination with CM03 . HoweverWe propose the following model for the synergistic effect between SAHA and CM03. SAHA, by inhibiting HDACs, induces chromatin relaxation and the formation of euchromatin regions , resultiWe have previously reported that CM03 in cells produces a modest DNA damage response , althougIt is also possible that the CM03-mediated downregulation of epigenetic modifiers such as demethylases and methyltransferases can contribute indirectly to the overall synergistic effect by preventing a re-condensation of relaxed chromatin. For instance, PRDM16 is a methyltransferase that maintains the integrity of heterochromatin, and its impairment along with PRDM3 leads to the disintegration of heterochromatic structures  Therefor50 values of 10–15 nM in PDAC cell lines  patients. The current clinical choice of FOLFIRINOX  combination therapy is a demanding therapy and is restricted to those patients with good performance status . Gemcita2O. SAHA  was obtained from Cayman Chemical Co. . It was used without further purification and was dissolved in dimethyl sulfoxide (DMSO). CM03 and gemcitabine were dissolved in phosphate-buffered saline (PBS). Stocks of 1 mM and 10 mM were prepared for each compound and kept frozen and away from light. Prior to addition to cell culture media, compounds were filtered using a 0.22 µm pore-size filter unit to maintain sterility.CM03 had been previously synthesized and purified as described  and was l-glutamine and 4500 mg/L glucose from Sigma-Aldrich (Cat. No. D6429). Media were supplemented with 10% FBS from ThermoFisher  (Cat. No. 10270106) and antibiotics 100 U/mL penicillin and 0.1 mg/mL streptomycin from Sigma-Aldrich (Cat. No. P4333). With MIA PaCa-2 and its germ-resistant form, 2.5% horse serum from ThermoFisher (Cat. No. 16050130) was also added. All cell lines were cultured in humidified incubators at 37 °C and 5% CO2 and passaged for 2–3 days. The cell lines were routinely tested to ensure that they were mycoplasma-free by a RT-qPCR-based method.MIA PaCa-2 (Cat. No. CRL-1420) and PANC-1 (Cat. No. CRL-1469) cell lines were obtained from ATCC. Parental and related gemcitabine-resistant cell lines, developed in-house, were all maintained in Dulbecco’s Modified Eagle’s Medium (DMEM) and already contained 2 mM Gemcitabine-resistant cell lines were generated from parental MIA PaCa-2 and PANC-1 pancreatic cancer lines by incrementally increasing the gemcitabine concentration in the culture medium over extended periods of time, as previously described . The res50 of each compound. After treatment, cells were fixed with 10% trichloroacetic acid (TCA) and cell viability was measured using a version of the sulforhodamine B (SRB) assay [First, 2000 cells/well of two pancreatic cancer cell lines (MIA PaCa-2 and PANC-1) or their generated gemcitabine-resistant form (GR3-MIA and GR3-PANC-1) were seeded in 96-well plates and incubated overnight. The drug combination SAHA + CM03 was evaluated in the synergy experiments. Cells were treated with two-drug combinations or single drugs for 96 h. A matrix format of 8 × 5 was used to assess all two-drug combinations using a range of concentrations several-fold above below the GIB) assay . https://www.proteinsimple.com/wes.html) to run the cell lysates and detect proteins of interest according to the manufacturer’s instructions. Antibodies used were for cleaved PARP , γ-H2AX , and GAPDH .MIA PaCa-2 and PANC-1 cells were seeded and treated with 0.4 µM CM03, SAHA (1 µM for MIA PaCa-2 and 4 µM for PANC-1) or in combination of both compounds, at these concentrations. The treatment continued for 24 h in MIA PaCa-2 cells and for 48 h in PANC-1 cells because 24 h treatment was insufficient for observing its effect on PANC-1 cells. Untreated or vehicle treated (DMSO) cells were used as a control. Cells were collected and lysed with RIPA lysis buffer  supplemented with protease and phosphatase inhibitors . The lysate concentration was quantified using the Pierce™ BCA Protein Assay Kit . Capillary-based automated Western blotting was performed on a Wes machine  based on the Chou-Talalay non-constant ratio method [For the SRB assays, the fraction affected was determined by taking the mean absorbance at 540 nm for each treated cell group expressed as a fraction of the absorbance of untreated or vehicle control-treated (DMSO) control cells. All experiments were performed in triplicate, and the mean ± SEM values were determined from at least three independent experiments. The combination index (CI) values were calculated using the CalcuSyn (version 2.0) software package , and the resulting intensity values were normalized to the loading control GAPDH. The one-way ANOVA test was used to determine the statistical significance of any changes in protein levels. Data represents mean ± SEM of 3 independent experiments. *


Expressions of genes related to cell cycle control  were evaluated by qRT-PCR by the LightCycler 480 System (Roche). GAPDH and ACTB housekeeping genes were used for the normalization of gene expressions. IC50 value of CAPE in MCF-7 cells was calculated as 75µM. It was shown that IC50 dose of CAPE induced significant upregulation in expressions of cell cycle control genes, compared to control cells. CAPE increases the expression of genes that are important in cell cycle control, suggesting that this component can be used as an effective chemopreventive agent in breast cancer cells. We aimed to find the effect of caffeic acid phenethyl ester (CAPE) on the expression profiles of cell cycle control genes in breast cancer cell line (MCF-7). The cytotoxic effect of CAPE on MCF-7 cell line was found with an XTT analysis. Total RNA was isolated from the cells exposed to IC RB1) is known as a tumor suppressor. RB1 mutations include all familial and sporadic forms of retinoblastoma. The RB gene is a common mutation in various human cancers . In our study, the increase in the expression of ATM, CDC34 was found to be significant. ATM and CDC34 genes can contribute to cancer treatment as cell cycle regulators. A decrease in CDK5RAP1 was found to induce cell cycle arrest and apoptosis by ROS/JNK signaling pathways in MCF-7 cell lines. CDK5RAP1 is very important in the development of clinical treatments for cancer [Silencing of CDC34 could lead to the inhibition of cell proliferation and suppression of colony formation . Increasr cancer . In addir cancer . The inc


Predicting the response of cancer cell lines to specific drugs is an essential problem in personalized medicine. Since drug response is closely associated with genomic information in cancer cells, some large panels of several hundred human cancer cell lines are organized with genomic and pharmacogenomic data. Although several methods have been developed to predict the drug response, there are many challenges in achieving accurate predictions. This study proposes a novel feature selection-based method, named Auto-HMM-LMF, to predict cell line-drug associations accurately. Because of the vast dimensions of the feature space for predicting the drug response, Auto-HMM-LMF focuses on the feature selection issue for exploiting a subset of inputs with a significant contribution.This research introduces a novel method for feature selection of mutation data based on signature assignments and hidden Markov models. Also, we use the autoencoder models for feature selection of gene expression and copy number variation data. After selecting features, the logistic matrix factorization model is applied to predict drug response values. Besides, by comparing to one of the most powerful feature selection methods, the ensemble feature selection method (EFS), we showed that the performance of the predictive model based on selected features introduced in this paper is much better for drug response prediction. Two datasets, the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) are used to indicate the efficiency of the proposed method across unseen patient cell-line. Evaluation of the proposed model showed that Auto-HMM-LMF could improve the accuracy of the results of the state-of-the-art algorithms, and it can find useful features for the logistic matrix factorization method.https://github.com/emdadi/Auto-HMM-LMF.We depicted an application of Auto-HMM-LMF in exploring the new candidate drugs for head and neck cancer that showed the proposed method is useful in drug repositioning and personalized medicine. The source code of Auto-HMM-LMF method is available in  Computational models for personalized medicine make it possible to understand cancer cell lines on the basis of genomic information. This knowledge makes it possible to recommend individualized therapies to patients with different types of cancer by measuring drug responses. Many effective anticancer drugs have already been developed for each cancer type, such as breast cancer, lung cancer, ovary cancer and Brain cancer. For example, docetaxel, paclitaxel, carboplatin, cisplatin, vinorelbine and eribulin are just a few examples of drugs used to treat breast cancer. Since drug response to cancer treatment depends on multiple factors such as the patient's genomic profile, this process is a complicated problem in cancer treatment. These challenges have generated large-scale experiments on human cancer cell lines and various anticancer drugs. For instance, two datasets Genomics of Drug Sensitivity in Cancer  (GDSC) aSeveral recommender system-based models were proposed for predicting drug response. Wang et al. adopted a similarity-regularized matrix factorization (SRMF) method to predict anticancer drug responses of cell lines using the gene expression profile in cell lines and drugs’ chemical structures. They indicated that rapamycin (an mTOR inhibitor) could be a new therapeutic agent for non-small cell lung cancer . SuphaviIdentifying the optimal subset of features from many genetic candidate features is a crucial issue for classification models for predicting drug response. Thus, a large number of algorithms have proposed using different approaches for feature selection. Xu et al. proposed AutoBorutaRF method based on feature selection for predicting drug response. This method first built an autoencoder network, and it used Boruta algorithm  to selecThis study presents a feature selection-based method for drug response prediction, named Auto-HMM-LMF, to efficiently predict cell line-drug associations. Gene expression profile, copy number alteration, single-nucleotide mutation, tissue type information of the cell line, and drugs’ chemical structure information were incorporated. Two strategies based on autoencoder and hidden Markov model-multinomial mixture model are used for selecting the essential features of input information. The autoencoder networks are applied on gene expression profile, copy number alteration data. Also, hidden Markov model and multinomial mixture model are applied on mutation data. A proper evaluation of the Auto-HMM-LMF method using tenfold cross-validation was carried out to compare it with the state-of-the-art methods. Results show its performance is superior for the tested data sets. Also, by comparing to the ensemble feature selection method (EFS), we showed that two considered strategies for feature selection in the Auto-HMM-LMF method could select proper features that significantly improved the prediction result.EXP) is constructed using selected features. Similarly, the similarity matrix (SimCNV) is created using the selected feature by applying the autoencoder feature selection method on copy number alteration information. In the next step, the similarity matrix (SimMUT) is generated using a novel feature selection approach based on the hidden Markov model and multinomial mixture model on single-nucleotide mutation data. Two similarity matrices (SimIC50) and (SimTISSUE) are achieved using IC50 values of cell lines across the drugs and tissue type information of each cell line, respectively. Finally, for constructing the latent vectors for each cell line and the drug and predicting whether the cell line is sensitive to the drug or not, the logistic matrix factorization method is applied. For assigning the IC50 values to two labels, sensitivity and resistance, we used the strategy introduced in previous researches i,j=1,….,t which ai,j is the transition probability from qi to qj.A = i=1,…,t, j=1,…,s where ei,j is the probability that state qi emits cj.E =  is the total number of times the ith mutation category were observed as isolated mutations in samples. By applying vector T to the initialized vector g and emission matrix E, their estimated values were achieved.1, …, OT, which is decomposed into close and isolated subsequences,{C1, I1, C2, I2, …, Ck1, Ik2}, is formulated as follows:After training two above models, the probability of sequence occurrence Ot), the state with maximum probability value (Qt) is obtained from the following formula:The Viterbi algorithm  was applFinally, we append these two most probable paths of states to construct the final path corresponding to the patient. Then, the numbers of observed states (signature) are calculated as signature frequency per sample or signature activities for each path. For example, a patient with BRCA cancer has a vector with size 12 corresponding to the number of signatures, and the elements of this vector are calculated based on the number of times each state is observed in the final path.For considering the relationship between signature activities and gene expression profiles for each patient, we downloaded the gene expression files for patients from the International Cancer Genome Consortium Data Portal . The numSince similar cell lines and similar drugs may have similar drug responses, the similarities between cell lines and drugs can improve drug response prediction , 18.The similarity matrices are required for the identification of the nearest neighbors in the logistic matrix factorization model. Gene expression profile, copy number alteration, single-nucleotide mutation, and tissue type information are used for cell line similarity, and chemical structures of drugs are used for drug similarity. So, five similarities between each pair of cell lines and the similarity between each pair of drugs are defined as follows:EXP) is the similarity based on the selected features of the gene expression profile, in which the numbers of identified essential genes for gene expression profile by autoencoder are 798 and 1189 for two datasets GDSC and CCLE, respectively. SimEXP is defined as the Pearson correlation between the gene expression vector of each pair of n cell lines, arranged in an n × n matrix.(SimCNV) is the similarity based on the selected features of copy number alteration data, which 67 and 127 useful features selected by autoencoder in GDSC and CCLE, respectively. SimCNV matrix is defined as an n × n matrix by Pearson correlation between the copy number alteration vector of each pair of cell lines.(SimMUT) is the similarity based on the selected features of single nucleotide mutation information by the hidden Markov model and multinomial mixture model. 22 and 67 essential genes identified by this strategy from GDSC and CCLE datasets, respectively. Then, the Jaccard similarity is applied on each pair of single nucleotide mutation vectors corresponding to n cell lines, and SimMUT is constructed as an n × n matrix.(SimIC50) is the similarity between cell lines based on their IC50 values. This definition of similarity between cell lines proposed by Liu is based on the correlation between response IC50 values of the cell lines n×n that represents cell line neighborhood information as follow:Since the correlation coefficient between each pair of the above similarity matrices is very low, there is no collinearity between matrices, and they can be linearly combined. We constructed an integrated matrix similarity, SimDRUG). For each drug, a zero–one vector of size 881 is considered where 881 is the number of known chemical substructures of a drug. In this vector, 1 indicates the presence of a substructure of the drug and 0 otherwise. SimDRUG = [SDij]m×m is constructed as an m × m matrix by Jaccard similarity between each of the chemical substructures vector corresponding to the m drugs. For a drug di, the set Nk(di) denotes the k-most similar drugs to di (except di) using SimDRUG matrix. The adjacency matrix, B = [bij]m×m, describes the drug neighborhood information as follows:The similarity between drugs is constructed based on chemical substructures , Ec and d = (Ed + T) as diagonal elements (n is the numbers of cell lines). Ed and IC50) matrix are unknown, the (SimCL) matrix cannot be used for finding the k-nearest neighbors for the new cell line. We used the strategy introduced in the DSPLMF method for estimation (SimIC50) matrix. DSPLMF method is designed a Decision Tree Classifier model for estimation (SimIC50) matrix using the gene expression profile, copy number alteration, and single-nucleotide mutation information of the new cell line [i is sensitive to the drug dj can be modeled as a logistic function as follows:For drug response prediction of cancer cell lines from GDSC and CCLE datasets using selected features, the DSPLMF method introduced based on the logistic matrix factorization method  is appliectively . Two parmentclasspt{minimaFinally, a threshold is applied on probabilities to assign a sensitive or resistance class to each new cell line-drug pair.Using the feature selection approaches is one of the common methods to reduce the dimensions of the features in drug response prediction problems. In some of the previous predictive methods, such as AutoBorutaRF, the autoencoder approaches are used for selecting significant features of genomic information. One of the most powerful methods of selecting features is the EFS method proposed by Neumann et al. The EFS method integrated eight different feature selection methods and normalized all individual outputs to a common scale, an interval from 0 to 1 , 22. FirTP (true positive): The number of cell lines labeled with sensitivity and predicted as sensitivity.TN (true negative): The number of cell lines labeled with resistance and predicted as resistance.FP : The number of cell lines labeled with resistance and predicted as sensitivity.FN : The number of cell lines labeled with sensitivity and predicted as resistance.We compared the Auto-HMM-LMF method to six classification models, DSPLMF, EFS-LMF, CaDRReS, SRMF, AutoBorutaRF, and SVM-RFE for different metrics. DSPLMF and AutoBorutaRF are designed as the classification models, but the CaDRReS and SRMF methods predicted IC50 values as output. So, for comparison of these models with the Auto-HMM-LMF and EFS-LMF methods, we applied the median of predicted IC50 values for each drug as a classification threshold. If the predicted IC50 value corresponding to a cell line-drug pair is smaller than this threshold, the sensitive class was assigned to it; otherwise, it was labeled with resistance class. Seven metrics Accuracy, Recall, Precision, Specificity, F1Score, Matthews correlation coefficient (MCC) and area under the receiver operating characteristic curve (AUC) are used that; these criteria are formulated as follows:Tables To demonstrate the Auto-HMM-LMF method’s performance in different tissue types, we examine whether our proposed method can achieve good performance when considering specific cell line tissue types. In this way, 73 Haematopoietic and lymphoid cell lines in the GDSC dataset are considered, and seven criteria evaluate the Auto-HMM-LMF method. We trained the Auto-HMM-LMF method by these cell lines, and we applied a tenfold cross-validation approach for drug response prediction of considered cell lines. As is shown in Table We plotted the Bar chart of Pearson correlation coefficients of observed drug responses and predicted values for 24 drugs in the CCLE dataset. As is shown in Fig. Drug repositioning is the process of selecting a known drug for an alternative pharmacological purpose. For this issue, we considered 37 US Food and Drug Administration (FDA) approved drugs that were not tested in the GDSC dataset from the study of Choi et al. . The AutAzacitidine: Azacitidine is a type of drug called a hypomethylating agent, and a study reported that Azacitidine and Cisplatin are effective for the treatment of head and neck cancer .Regorafenib: Regorafenib is an oral multi-kinase inhibitor that targets receptor tyrosine kinase (RTK). Klinghammer et al. established a panel of 65 head and neck squamous cell carcinoma, and they demonstrated that combinational treatment of regorafenib and Everolimus is useful in these patients .Everolimus: Everolimus is used as an immunosuppressant to prevent rejection of organ transplants in the treatment of cancer. Recently, a study showed that patients with TP53 mutations benefited significantly from Everolimus in head and neck cancer .Fulvestrant: Fulvestrant is a drug used to treat hormone receptor (HR)-positive metastatic. Grünow et al.  showed tInterferonAlf a–2B: InterferonAlf a–2B is an antiviral or antineoplastic drug that is an effective treatment in head and neck cancer .Porfimer: Porfimer is a photosensitizer, and it is used in radiation therapy in cancer treatment. An in vivo study suggested that this drug can be used in treatment for HNSC patients .lmiquimod: Imiquimod (INN) is a prescription drug that acts as an immune response modifier used to treat basal cell carcinoma. The study showed that topical Imiquimod might offer a reasonable and well-tolerated palliative treatment option for patients .Decitabine or 5–aza–2′–deoxycytidine is a nucleic acid synthesis inhibitor for cancer treatment. Cisplatin resistance in head and neck squamous cell carcinoma reduces survival. Viet et al.  showed tHydroxyurea: Hydroxyurea is an anti-cancer agent used to treat melanoma, resistant, recurrent, and metastatic cancer types. A study displayed Hydroxyurea is a single active agent in head and neck cancer. It has been used clinically as a radiation-enhancing drug with radiotherapy .V andetanib: V andetanib acts as a kinase inhibitor of several cell receptors, and it is an anti-cancer drug for the treatment of cancer cell lines. Sano et al.  approvedCarf ilzomib: Carf ilzomib is an anti-cancer drug acting as a selective proteasome inhibitor. By upregulation of pro-apoptotic Bik, Carf ilzomib and ONX0912 potently induced apoptosis in HNSC cell lines .As it can be seen in Fig. These results indicate that the Auto-HMM-LMF model can be useful in drug repositioning. Also, five drugs  may be novel therapeutic drugs for HNSC.Since the numbers of cell lines and drugs in the GDSC dataset are higher than the CCLE dataset, we tuned the hyperparameters on the GDSC dataset, and we used the obtained values of the hyperparameters in both datasets. In this way, the tenfold cross-validation procedure is applied to GDSC, and hyperparameters are determined by maximizing the AUC criterion.The learned hyperparameters using the GDSC dataset are shown in Table This paper proposed the Auto-HMM-LMF method based on feature selection approaches and logistic matrix factorization strategy to predict drug response. The proposed prediction model showed higher predictive efficiency than the existing computational models. Also, we demonstrated that the Auto-HMM-LMF model could be useful in drug repositioning. So, we identified five drugs  for HNSC treatment. To illustrate the biological significance of the features selected by the hidden Markov model (HMM) and multinomial mixture model (MMM) on mutation data in the Auto-HMMLMF method, we further consider cancer cell lines related to breast cancer (BRCA) and two important processes, namely MMR and HRD. This study selected 30 significant genes by considering the Spearman correlation coefficient between their gene expression file and signature activity for 12 signatures of BRCA cancer cell lines. Among these genes, the gene expression of four genes namely PMS2, MLH1, MSH2, and MSH6 has high Spearman correlation coefficient with signatures 6, 20, and 26 activities. The results of the Spearman correlation between the expression of these genes and three mutation signature activities in BRCA are shown in Fig. Also, the expression of ten other selected genes by proposed model, namely BRCA1, BRCA2, ATM, CHEK2, MRE11A, NBN, FANCA, PALB2, RAD51C, RAD50 has high Spearman correlation coefficient with activity of mutation signature 3. The Spearman correlation coefficients between the expressions of these genes with a signature 3 activity are shown in Fig. In this study, we developed a feature selection-based method, Auto-HMM-LMF, to predict cancer cell lines’ response to drugs in the GDSC and CCLE datasets. For feature selection of gene expression and copy number variation data, two autoencoder networks are designed. For feature selection of single nucleotide mutation information, the novel approach based on the hidden Markov model (HMM) and multinomial mixture model (MMM) is applied. Auto-HMM-LMF shows better overall prediction performance than the state-of-the-art prediction methods. Also, by comparing to one of the most powerful feature selection methods, the EFS method, we showed that the performance of the predictive model based on selected features introduced in this paper is much better for drug response prediction. Also, we suggest that the proposed model can be useful in numerous therapeutic research areas, such as drug repositioning and personalized medicine. Finally, we found substantial evidence that the selected features and predicted responses by Auto-HMM-LMF have significant consistency with many previous studies.Additional file 1. The results of the correlation between some genes and signature activity in 14 cancer types with high Spearman correlation coefficients.Additional file 2. The similarity matrices based on tissue type information corresponding to GDSC dataset.Additional file 3. The similarity matrices based on tissue type information corresponding to CCLE dataset.Additional file 4. The scatter plots of observed and predicted drug responses by the Auto-HMM-LMF model of 20 drugs in the CCLE dataset.


Identifying and removing multiplets are essential to improving the scalability and the reliability of single cell RNA sequencing (scRNA-seq). Multiplets create artificial cell types in the dataset. We propose a Gaussian mixture model-based multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes multiplets through sample barcoding, including cell hashing and MULTI-seq. GMM-Demux uses a droplet formation model to authenticate putative cell types discovered from a scRNA-seq dataset. We generate two in-house cell-hashing datasets and compared GMM-Demux against three state-of-the-art sample barcoding classifiers. We show that GMM-Demux is stable and highly accurate and recognizes 9 multiplet-induced fake cell types in a PBMC dataset. Droplet-based single cell RNA sequencing (scRNA-seq) , 18, 48 Recently, emerging sample barcoding technologies, such as cell hashing  or MULTImulti-sample multiplets (MSMs), droplets that contain more than one cell from more than one HTO sample; (2) single-sample multiplets (SSMs), droplets that contain more than one cell from a single HTO sample; and (3) singlets, droplets that contain a single cell. We combine singlets and SSMs into a single category called single-sample droplets (SSDs) to differentiate them from MSMs. The relationship between MSM, SSM, singlet, and SSD is summarized in Fig. There are three types of droplets in a sample barcoding scRNA-seq dataset: 1)  multi-sapure-type GEMs whereas GEMs that contain multiple cell types are named phony-type GEMs. An illustration of phony-type GEMs and pure-type GEMs is provided in Fig. GEMs can also be classified based on the number of cell types enclosed in them. GEMs that contain a single cell type are named + B cells, CD3+ T cells, and also a CD3+CD19+ double-positive T-B cell GEM cluster. Similarly, in the CD4-CD8 scatter plot of all CD3+ T cells, besides the CD4+ helper T cells and CD8+ cytotoxic T cells, there also exists a CD4+CD8+ helper-cytotoxic T cell GEM cluster. Both clusters are highlighted in red circles. Existence of such PBMC types at the observed frequencies is unlikely, as CD3+CD19+ cells and CD4+CD8+ T cells are believed to be extremely rare , x∈. Hence, the singlet formula used by Demuxlet under d0=0.01 and Y0=1K can be approximately explained by GMM-Demux as randomly partitioning Y cells among a total of X=100K cell-assay droplets. Despite apparent similarities between their formulas, GMM-Demux and Demuxlet employ different underlying statistical mechanics. Demuxlet uses a discriminative model, which uses regression to subjectively model the multiplet rate as a parametrized curve. GMM-Demux, on the other hand, uses a self-explanatory, generative model that directly simulates the GEM formation process. The generative model allows GMM-Demux to estimate the MSM rates of pure-type and phony-type GEM clusters in a sample barcoding dataset, while the discriminative model of Demuxlet does not. The generative model also enables GMM-Demux to accurately simulate multiplets, including both pure-type and phony-type GEMs; singlets; SSMs; and MSMs, whereas Demuxlet cannot.There are only a few prior studies on modeling multiplet rates. Demuxlet , a genetX, in this case) approaches infinity . According to the GEM formation model, which models partitioning of cells into droplets with a binomial distribution, we have:Let We derive the model parameters by computing:Y is the user-provided total number of cells loaded for library preparation, which can be obtained from the hemocytometer.where Y), the planned number of samples for sample barcoding (M), the estimated number of droplets (X), and the droplet capture rate (rcap) in library preparation as inputs, and it computes the estimated multiplet rates. The online experiment planner assumes cells are evenly distributed among M samples.The online sample barcoding experiment planner estimates the singlet, SSM, and MSM rates of a planned sample barcoding experiment via the GEM formation model. Specifically, it takes the estimated number of cells  rate, defined as the estimated number of SSMs among SSDs. Mathematically, the RSSM rate is defined as:The RSSM rate marks the overall quality of a sample barcoding dataset. It represents the percentage of irremovable multiplets among SSDs, after removing all MSMs in the dataset. If the RSSM rate of the estimated outcome is too high, then the planned experiment should be aborted, as the anticipated outcome will be too noisy for downstream analysis. While dividing the cell assay into more samples drives down the RSSM rate, as it reduces The online experiment planner computes the multiplet rates as follows:M, does not affect the singlet rate. The singlet rate is solely determined by X and Y. However, a greater M reduces the SSM rate and increases the MSM rate. Therefore, we conclude that dividing a cell assay into more samples by sample barcoding transforms more SSMs into MSMs. Transforming SSMs into MSMs improves the quality of the dataset. With fewer SSMs, the RSSM rate of the dataset decreases. In comparison, having more MSMs does not affect the quality of the dataset, as MSMs are removed by GMM-Demux.The above equations show that the number of samples, rcap, the online experiment planner also computes the estimated number of cell-enclosing GEMs in the final output, as well as the estimated number of SSDs after removing MSMs. The number of cell-enclosing GEMs, #non-emptyGEM=#non-emptydrops·rcap (#non-emptydrops is computed in Eq. (#SSD=#non-emptyGEM·(1−P(i∈MSM)).Given d in Eq. ). The nuY and M are user-controlled while X and rcap are largely dictated by the library preparation equipment. However, based on our observations, we found that X mostly varies between 65K and 80K. To account for the wide ranges of variability of the inputs, the online experiment planner uses sliders for selecting X, Y, M, and rcap, which have ranges of 60K– 100K, 1K– 80K, 1–20, and 0–1, respectively. The online experiment planner supports dynamic updates. It computes the estimated multiplet rates in real time as the user updates input parameters. In practice, we recommend that users profile their library preparation equipment once for the total number of droplets (X) in a sequencing run, by performing a small-scale sample barcoding experiment, and use the profiled X (included in the GMM-Demux output) in planning future experiments.Among all four inputs, In novel cell-type identification, a cell-type classifier is used to group GEMs into clusters. Each cluster is assumed to represent a unique cell type. Clusters with average expression profiles that do not match any known cell types are identified as novel cell types .After clustering, phony-type GEMs are grouped into distinct clusters. Phony-type GEM clusters may be incorrectly identified as novel cell types, as their expression profiles do not match known cell types, generating false discoveries. GMM-Demux rectifies true novel cell types by validating if the alleged novel cell-type GEM cluster contains mainly pure-type GEMs. Based on the GEM composition in the cluster, GMM-Demux classifies GEM clusters into three categories: pure-type GEM clusters, phony-type GEM clusters, and mixture clusters. Phony-type GEM clusters contain mostly phony-type GEMs. Pure-type GEM clusters contain mostly pure-type GEMs. Mixture clusters contain large quantities of both pure-type and phony-type GEMs.G represent a GEM cluster. GMM-Demux classifies G by examining the MSM ratio of G. For simplicity, we assume cells are equally randomly divided into the M sample barcoding samples. If G is a phony-type GEM cluster, the MSM ratio of G must be very high. Elaborated in Additional file 1: Section S3, the expected MSM ratio of a phony-type cluster approaches and exceeds G is a pure-type GEM cluster, its MSM ratio should not be greater than the MSM ratio of the entire sample barcoding dataset, which is much smaller than G: in a phony-type GEM cluster, all GEMs are multiplets; hence, the MSM ratio of G, G, respectively; in a pure-type GEM cluster, however, we have G. By comparing the two ratios, we observe that pure-type GEM clusters include singlet counts in the denominator, whereas phony-type GEM clusters do not. As a result, the MSM ratio is much higher in phony-type GEM clusters than in pure-type GEM clusters. Complex situations where cells are not evenly distributed among sample barcoding samples are discussed in Additional file 1: Section S3.Let phony-type hypothesis and the pure-type hypothesis, which assume G being a pure-type or a phony-type GEM cluster, respectively. GMM-Demux tests both hypotheses with the binomial test and computes a p value for each hypothesis. Based on the hypothesis testing results, GMM-Demux classifies G as a pure-type GEM cluster, a phony-type GEM cluster, or a mixture cluster. Details of the hypothesis tests are provided in Additional file 1: Section S3.GMM-Demux uses hypothesis testing to measure the confidence of each classification. GMM-Demux prepares two hypotheses, the G, GMM-Demux recommends different actions. Being classified as a phony-type GEM cluster suggests that the proportion of pure-type GEMs in G, if there exists any, is extremely small and most GEMs in G are phony-type GEMs. GMM-Demux recommends excluding G from further analysis. Being classified as a mixture cluster suggests that G mixes pure-type GEMs and phony-type GEMs together and has non-trivial numbers of GEMs in both categories. This is often a result of poor clustering quality where G becomes a super-cluster over several pure-type and phony-type GEM clusters. GMM-Demux recommends refinement over the clustering method and subdividing G into pure-type GEM and phony-type GEM sub-clusters. Finally, being classified as a pure-type GEM cluster suggests that it is plausible that G defines a real cell type. Further analysis over G is recommended.Based on the classification result of Y), as inputs, and it outputs a double column table as the classification result. The row indices of the output table are GEM barcodes. The two columns are the classification of each GEM and the confidence score of each classification, respectively. With M samples, GMM-Demux classifies GEMs into a maximum of 2M+1 classes. Besides the uncertain class, the negative class, and M SSD classes, there are M-sample class. Additionally, GMM-Demux produces a SSM rate summary file, which includes the SSM rate and the RSSM rate of each sample, and a summary file that includes the multiplet rates of the entire dataset. The summary file also includes the estimated number of cell-assay droplets (X) and the estimated droplet capture rate (rcap) of the library preparation equipment. Example outputs are provided in Additional file 1: Section S4.The GMM-Demux classifier is compatible with CellRanger-3.1.0 from 10X Genomics. It takes the sample barcoding data of post-filtering, non-empty droplets, in the market matrix (ṁtx) format, together with the estimated number of cells (Additional file 1 Supplementary figuresAdditional file 2 Supplementary tablesAdditional file 3 Review history


ABCG2 wt, the SNPs: 34 G > A and 421 C > A, or with empty vector (K562/ve). ABCG2‐expressing cells were enriched using magnetic sorting and the expression was verified using flow cytometry. Intracellular erlotinib concentrations were analyzed by LC–MS/MS after incubation with 1 µmol/L erlotinib for 60 minutes. All recombinant cell lines were confirmed carriers of the vector and expressed ABCG2. Differences in intracellular erlotinib concentrations were observed between K562/ve and K562 ABCG2 wt and between K562/ve and K562 ABCG2 34G > A , indicating that the cell lines carrying ABCG2 wt and ABCG2 34G > A actively transports erlotinib out of the cells. The ABCG2 34G > A cell line had a higher transport capacity compared with ABCG2 wt after adjusting for ABCG2 expression . No differences were observed between K562/ve and K562 ABCG2 421 C > A. Genetic polymorphism in the ABCG2 gene has an influence on the transport of erlotinib which can contribute to the observed variation in erlotinib pharmacokinetics and toxicity.Single nucleotide polymorphisms (SNPs) in the gene for multidrug resistance protein ABCG2, an erlotinib transporter, is a possible contributor to the interindividual variation observed in erlotinib pharmacokinetics and toxicity. Therefore, the aim was to study erlotinib efflux by ABCG2 wild‐type (wt) and ABCG2 polymorphic variants in the K562 cell line. The chronic myeloid leukemia K562 cell line, neither expressing EGFR nor ABCG2, was transduced with vectors containing the  ABCATP‐binding cassetteACNacetonitrileAmAcammonium acetateBCRPbreast cancer resistance proteinEGFRepidermal growth factor receptorEYFPenhanced yellow fluorescent proteinFBSfetal bovine serumHPLChigh‐performance liquid chromatographyLC–MS/MSliquid chromatography‐tandem mass spectrometryMDRmultidrug resistanceMeOHmethanolMFImean fluorescence intensityNSCLCnon–small cell lung cancerPBSphosphate‐buffered salineSNPsingle nucleotide polymorphismTKItyrosine kinase inhibitorwtwild type1The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) erlotinib is used in the treatment of non–small cell lung cancer (NSCLC) patients harboring somatic EGFR mutations, including exon 19 deletion and exon 21 L858R substitution.Erlotinib is a targeted therapy that is administered orally once daily. Large interindividual variation is observed in erlotinib pharmacokinetics. Plasma drug concentrations have been associated with adverse drug reactions,The membrane‐associated protein ABCG2, also known as the breast cancer resistance protein (BCRP), belongs to the ATP‐binding cassette (ABC) transporter family Horsey Aaron et al.Concentrations of erlotinib in tumors are found to vary between <5 nmol/L and 1.1 µmol/L.ABCG2 nonsynonymous single nucleotide polymorphisms (SNPs) 34 G > A (V12M) and 421 C > A (Q141K) are vastly studied. The ABCG2 421C > A polymorphism is known to generate a lower protein expression compared to ABCG2 wild type (wt),ABCG2 34 G > A shows expression levels comparable to ABCG2 wt.In general, the ABCG2 421 C > A carrying cell line were not different compared to the control cell line.Earlier ABCG2 in vitro studies with erlotinib have determined that erlotinib is an ABCG2 substrate and that ABCG2 wt is more efficient in transporting erlotinib compared to control cell lines.ABCG2 wt and ABCG2 polymorphisms influence the intracellular concentrations of erlotinib after drug exposure. Intracellular accumulation of erlotinib was assessed in the chronic myeloid leukemia cell line, K562, stably transduced with ABCG2 wt, the ABCG2 polymorphisms 34 G > A and 421 C > A, and empty vector. By using a non–EGFR‐expressing leukemic cell line, the effect of erlotinib on cell survival is minimized.Therefore, we aim at investigating how 22.1Reference substances for erlotinib and erlotinib‐D6 were purchased from Toronto Research Chemicals. Acetonitrile (ACN) of LC–MS grade was purchased from Merck AB and methanol (MeOH) of HPLC grade from Thermo Fischer Scientific. Ammonium Acetate (AmAc) of analytical grade was obtained from Sigma Aldrich.2.22. The cells were maintained by replacement or addition of fresh media to a concentration of 200 000 cells/ml every 3‐4 days. Cell culture reagents were obtained from Life Technologies. All cell lines were tested negative for mycoplasma infection.The parental K562 cell line (ATCC‐LCG Standards) and the transduced K562 cell lines were cultivated in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS) and 2% penicillin/streptomycin at 37°C with 5% CO2.3http://www.proteinatlas.org).The K562 cell line was considered to be a suitable modeling system to study how erlotinib is transported by ABCG2 as K562 naturally neither express ABCG2 nor EGFR  retroviral vector (MIY) by utilization of Not1 restriction enzymes. 293T cells were transfected with MIY‐ABCG2 and helper vectors VSVG and POL‐GAG following transduction of K562 using spin infection of the collected viral 293T supernatant. An empty MIY vector was transduced into K562 to serve as a control cell line.K562 was stably transduced with ABCG2 (K562/ve), a cell line with ABCG2 wt (K562 ABCG2 wt), and cell lines with the ABCG2 variants 34 G > A (K562 ABCG2 34) and 421 C > A (K562 ABCG2 421).The final recombinant K562 cell lines were a control cell line with MIY vectors without 2.47 cells were labeled with microbeads and separated on an LS column with a magnetic separator.Enrichment of ABCG2‐expressing cells was performed using magnetic sorting with microbeads conjugated to monoclonal anti‐human ABCG2 (CD338) antibodies (Miltenyi Biotec). Briefly, 1 × 106 cells were labeled with the ABCG2 antibody and washed twice with phosphate‐buffered saline (PBS).Expression of ABCG2 and EYFP was quantified using flow cytometry (Beckman Coulter) after extracellular staining ABCG2 with PerCP‐Cy™5.5 Mouse Anti‐Human CD338 antibody obtained from BD Biosciences. Before analysis, 1 × 102.52 the cells were transferred to a 15 mL tube and centrifuged at 1000g for 5 minutes at room temperature and washed twice with 5 mL cold PBS. The cells were lysed with 200 µL of MeOH containing 6.25 ng/mL erlotinib‐d6 and 200 µL of 10 mmol/L AmAc. The samples were thoroughly vortexted for 10 seconds before centrifugation at 17 000g for 10 minutes at 4°C. The supernatant was transferred to a 96‐well plate and stored at −20°C before LC–MS/MS analysis. Two independent experiments were carried out in triplicates for all cell lines except for K562/ve that contained one replicate in the second experiment.The cell lines were seeded at 400 000 cells/mL in a total volume of 5 mL and incubated with a final concentration of 1 µmol/L erlotinib. After 60 minutes of incubation at 37°C with 5% COThe ABCG2 transport capacity was determined by normalizing the intracellular concentration of erlotinib using the ABCG2 expression. The difference in intracellular erlotinib concentration between the average K562/ve and a replicate from an ABCG2‐expressing cell line was divided by the PerCP‐Cy5.5 response from the ABCG2‐expressing cell line adjusted for isotype expression (Δ ng/mL/Δ MFI).2.6Erlotinib and erlotinib‐d6 were analyzed using a previously described LC–MS/MS method.Calibration and quality control samples for erlotinib were separately prepared at 25, 75, 375, 1200, 375,0 and 5000 ng/mL and, 25, 75, 400, 1000, and 3750 ng/mL in 50% MeOH, respectively. To 50 µL of each sample, 200 µL of MeOH containing 6.25 ng/mL erlotinib‐d6 and 200 µL of 10 mmol/L AmAc was added. The samples were transferred to a 96‐well plate and stored at −20°C before LC–MS/MS analysis.2.7t test using the IBM SPSS Statistics, version 26 (IBM).Statistical differences in intracellular concentrations between cell lines were analyzed with one‐way ANOVA and Tukey HSD post hoc test and differences in transport capacity normalized for ABCG2 expression were analyzed with Student's independent 33.1ABCG2 transduced K562 cell lines confirmed EYFP expression in all transduced cell lines, Figure ABCG2 421 (mean fluorescent intensity (MFI) = 1.18) compared with K562 ABCG2 wt (MFI = 4.68) and K562 ABCG2 34 (MFI = 2.87). Despite low EYFP expression, a signal compared to the K562 (MFI = 0.39) was observed, suggesting that the K562 ABCG2 421 cell line harbor plasmids even though it is low. The EYFP expression in the control cell line K562/ve was extremely high (MFI = 223), Figure In the flow cytometry analysis of the ABCG2 wt (MFI = 49.6) and followed in decreasing order in K562 ABCG2 34 (MFI = 21.0) and K562 ABCG2 421 (MFI = 8.49), Figure ABCG2 wt, K562 ABCG2 34, and K562 ABCG2 421, respectively.The ABCG2 expression, determined based on the PerCP‐Cy5.5 response, was most prevalent in the K562 3.2The performance of the LC–MS/MS method for the quantification of erlotinib was monitored in each run (n = 2). Precision and accuracy for erlotinib in all quality control samples were < 5% and between 92% and 108%, respectively.3.3ABCG2 wt (P ≤ .001) and K562/ve and K562 ABCG2 34 (P ≤ .001), Figure ABCG2 421 were not significantly different from the K562/ve.No statistical differences in intracellular erlotinib concentrations were observed between the parental K562 cell line and the K562/ve cell line. All intracellular‐concentrations in ABCG2‐expressing cell lines were compared with K562/ve. Statistical differences were observed between the K562/ve and the K562 ABCG2 34 showed a higher erlotinib transport capacity compared to K562 ABCG2 wt, despite similar intracellular concentrations due to lower ABCG2 expression in K562 ABCG2 34 (P = .024). The normalized erlotinib transport capacity for K562 ABCG2 421 was widely spread and around zero.To account for variation in ABCG2 expression in the recombinant cell lines, the intracellular erlotinib concentrations were normalized to the ABCG2 expression, Figure 4ABCG2 SNPs 34 G > A and 421 C > A. The intracellular concentrations in K562 ABCG2 wt and K562 ABCG2 34 were significantly lower compared to the K562/ve indicating that the two cell lines actively transport erlotinib out of the cell, Figure ABCG2 34 exhibited a higher transport capacity compared to K562 ABCG2 wt, when the intracellular concentrations were normalized to ABCG2 expression, due to lower ABCG2 expression in K562 ABCG2 34, meanwhile similar amounts of erlotinib were transported out of the cells, Figure ABCG2 421 did not differ from K562/ve, and when normalized to ABCG2 expression, the ABCG2 transport capacity was widely spread and included negative values, Figures In this project, we have studied erlotinib efflux by K562 cells carrying wild‐type ABCG2 and the http://www.proteinatlas.org).The cell line used in this project, the chronic myeloid leukemia K562 cell line, expresses no or very low amounts of ABCG2 and EGFR (The control cell line, K562/ve, containing only an empty vector without the ABCG2 gene, exhibited a very high EYFP expression. This was probably due to the more successful transduction of the empty vector as it was smaller, 6.5kb, compared to the other vectors that additionally contained 4.5kb of ABCG2 cDNA. A more even distribution of EYFP expression among the cell lines would probably have been achieved if the size difference between the empty vector and the ABCG2 containing vectors had been smaller.ABCG2 wt and K562 ABCG2 34 exhibited significantly lower erlotinib intracellular concentrations indicating that erlotinib was actively transported out of the cells. It is in line with previous studies where ABCG2 34 showed similar expression levels and transport capacity as wild type in other studied substances.The two cell lines K562 ABCG2 421 in this study, but it may also be caused by the reduced plasmid content. The results in this study suggest that cells carrying the 421 C > A gene variant are less effective than those with ABCG2 wt and 34G > A to transport erlotinib out of the cell. Our results confirm earlier results that there is no difference between cells carrying the 421C > A and control cell line.It has previously been shown that 421C > A results in a lower ABCG2 expression.ABCG2 wt and K562 ABCG2 34 actively transports erlotinib by efflux while K562 ABCG2 421 exhibits a reduced erlotinib transport capacity. ABCG2 polymorphism can, therefore, be considered as a possible contributor to the interindividual variation observed in erlotinib pharmacokinetics and toxicity.In summary, we have studied the transport of erlotinib in the K562 cell lines transduced with ABCG2 wt and ABCG2 SNPs in this project. We have identified that erlotinib efflux is affected by SNPs in ABCG2 which leads to variation in intracellular concentrations. The results obtained in this study indicate that K562 The authors declare no conflicts of interest.Participated in research design: Green, Vikingsson, Svedberg, and Jacobs. Conducted experiments: Svedberg and Jacobs. Performed data analysis: Svedberg and Jacobs. Wrote or contributed to the writing of the manuscript: Green, Vikingsson, Svedberg, and Jacobs.


Wolbachia are intracellular endosymbionts of several invertebrate taxa, including insects and nematodes. Although Wolbachia DNA has been detected in ticks, its presence is generally associated with parasitism by insects. To determine whether or not Wolbachia can infect and grow in tick cells, cell lines from three tick species, Ixodes scapularis, Ixodes ricinus and Rhipicephalus microplus, were inoculated with Wolbachia strains wStri and wAlbB isolated from mosquito cell lines. Homogenates prepared from fleas collected from cats in Malaysia were inoculated into an I. scapularis cell line. Bacterial growth and identity were monitored by microscopy and PCR amplification and sequencing of fragments of Wolbachia genes. The wStri strain infected Ixodes spp. cells and was maintained through 29 passages. The wAlbB strain successfully infected Ixodes spp. and R. microplus cells and was maintained through 2–5 passages. A novel strain of Wolbachia belonging to the supergroup F, designated wCfeF, was isolated in I. scapularis cells from a pool of Ctenocephalides sp. cat fleas and maintained in vitro through two passages over nine months. This is the first confirmed isolation of a Wolbachia strain from a flea and the first isolation of any Wolbachia strain outside the “pandemic” A and B supergroups. The study demonstrates that tick cells can host multiple Wolbachia strains, and can be added to panels of insect cell lines to improve success rates in isolation of field strains of Wolbachia. Wolbachia is a genus of obligate intracellular endosymbiotic gram-negative bacteria of the family Anaplasmataceae in the order Rickettsiales. Wolbachia infect two phyla in the Ecdysozoa: the Arthropoda and the Nematoda, with a much broader range of host species in the former than in the latter. Although only one species, Wolbachia pipientis, has been formally described [Wolbachia is best known for its ability to induce five distinct reproductive manipulations in arthropod hosts (cytoplasmic incompatibility (CI), induction of parthenogenesis, male killing, feminisation and meiotic drive), all of which favour its spread by reducing resource competition from males (a dead-end host) or imposing a fitness cost on uninfected females [Wolbachia form obligate and putatively beneficial relationships with their hosts, including strains from supergroups C and D in nematodes and E in springtails [escribed , the genescribed ,3,4. Corescribed . Wolbach females ,7,8,9. H females ,11. In oingtails ,13.Wolbachia is found in most of the major groups of haematophagous arthropods, including biting Diptera and Hemiptera, fleas, lice and parasitic mites [Wolbachia strain) die early in development, is common in blood-feeding Diptera [Wolbachia strain in bedbugs is a nutritional mutualist, providing B vitamins for its host that are deficient in the blood meal [Wolbachia has long been of applied interest for disease control, as release of Wolbachia-infected male pest insects can suppress natural populations where the females are uninfected or harbour an incompatible strain [Wolbachia using antibiotics can safely clear adult worm infections, unlike conventional anthelmintics [Wolbachia infections can suppress the dissemination and transmission of pathogens in insects, especially when transinfected into a novel host [Wolbachia-infected Aedes aegypti to reduce the transmission of dengue and other arboviruses [ic mites ,24,25,26 Diptera ,25,27. Iood meal ,20. Wolbe strain . In filalmintics . Finallyvel host . This phoviruses .Wolbachia infections still remains ambiguous. While many studies have reported the presence of Wolbachia in ticks using molecular methods [Wolbachia, or if the bacteria are present within cells of species of the parasitic wasp genus Ixodiphagus [Wolbachia-positive Ixodes ricinus ticks are almost always positive for Ixodiphagus DNA [The order Ixodida is the only large group of haematophagous arthropods in which the status of  methods ,43,44,45diphagus  or otherdiphagus  or mitesagus DNA ,48.Wolbachia. To answer this, we first tested the ability of cell lines with known broad susceptibility to infection with intracellular bacteria, derived from I. ricinus, Ixodes scapularis and Rhipicephalus microplus, to support the replication of two laboratory strains of Wolbachia derived from different insect hosts. We then applied an I. scapularis cell line in an attempt to isolate Wolbachia or other intracellular bacteria from field-collected fleas in Malaysia.However, the question remains whether or not tick cells are capable of supporting infection and growth of I. scapularis cell lines ISE6 [I. ricinus cell line IRE11 [I. scapularis cell line IDE8 [R. microplus cell line BME/CTVM23 [The nes ISE6  and ISE1nes ISE6  and the ne IRE11  were maine IRE11 . The I. ine IDE8  was mainine IDE8  supplemeE/CTVM23  was mainIxodes spp. cell lines, the wStri strain, originally isolated from the small brown planthopper Laodelphax striatellus into the Aedes albopictus cell line NIAS-AeAl-2 [wAlbB strain, originally isolated from the mosquito Ae. albopictus into the Ae. albopictus cell line Aa23 [R. microplus cell line BME/CTVM23, the wAlbB strain, originally provided by Scott O’Neill, Yale University School of Medicine, was transferred from Aa23 cells to Ae. albopictus C6/36 cells [Drosophila medium  and Mitsuhashi and Maramorosch medium  supplemented with 10% FBS and 2 mM L-glutamine. Infected mosquito cell cultures were maintained at 28 °C with weekly medium change and occasional subculture.For experiments with S-AeAl-2  and kindS-AeAl-2 , was proine Aa23  and kind36 cells  maintainwStri in Ixodes spp. cell lines, cell-free Wolbachia were initially used to inoculate tick cell cultures. Wolbachia were released from heavily infected Ae. albopictus cells by forcibly passing infected cell suspensions through a 25 G needle. The resultant suspension was filtered through a 2 µm syringe filter and inoculated into tick cell cultures. To enhance infection rates, filtered Wolbachia were transferred to a 2 mL microfuge tube containing tick cells (1–2 × 106 cells/mL in 1.5 mL) centrifuged at 5000× g for 5 min and allowed to sit at room temperature for 30 min prior to seeding into culture flasks [wAlbB) or 32 °C (wStri). Once established in tick cell lines, Wolbachia were maintained by inoculating uninfected tick cell cultures with a suspension of infected cells at dilutions of 1:5 for wAlbB and 1:10–1:20 for wStri.For experiments with wAlbB and e flasks . CulturewAlbB in R. microplus cells, a 1.2 mL aliquot of resuspended infected C6/36 cells was diluted with a further 0.5 mL Schneider’s modified Drosophila medium, passed ten times through a bent 26-gauge needle and centrifuged at 1500× g for 5 min. A 0.3 mL aliquot of the supernate was added to a 2.2 mL culture of BME/CTVM23 cells in a flat-sided culture tube, and to an uninfected culture of C6/36 cells in L-15 (Leibovitz) medium supplemented as above but with 10% FBS, in a flat-sided tube. Cultures were incubated at 28 °C; once established, Wolbachia were subcultured by passaging resuspended infected BME/CTVM23 onto fresh cells at a dilution of ~1:10.For experiments with Ctenocephalides sp. fleas were collected from domestic cats from a village of indigenous people, also known as the Orang Asli, in Perak, Malaysia . All field sampling was conducted with the approval of the University of Malaya Institutional Animal Care and Use Committee as well as the Department of Orang Asli Development in Malaysia. Live fleas were immobilised at −80 °C for 15 min and identified to genus level using morphological keys [cal keys , followewAlbB and wStri, and at 8 months p.i. for cultures inoculated with flea organs, tick cells were resuspended by pipetting and cytocentrifuge smears were prepared from small aliquots (~50 µL) of cell suspension, stained with Giemsa and examined at ×500–1000 magnification. Wolbachia-infected BME/CTVM23 cells were prepared for transmission electron microscopy as described previously [Live infected tick cell cultures were monitored by weekly inverted microscope examination. At intervals of 1–7 weeks post inoculation (p.i.) for cultures inoculated with eviously .Wolbachia wsp, coxA, fbpA, ftsZ, hcpA and gatB genes was carried out according to published protocols [wStri-infected NIAS-AeAl-2 cells, wAlbB-infected Aa23 or C6/36 cells for Wolbachia-specific PCRs or Rickettsia raoultii-infected BME/CTVM23 cells [wAlbB-infected BME/CTVM23 cells and the absence of contaminating mosquito cells, qPCR amplification of fragments of the Wolbachia 16S rRNA and mosquito 18S rRNA genes was carried out as described previously [Wolbachia counts were normalised against tick cell counts generated by the amplification of fragments of the tick rpl6 gene as described previously [At intervals, 200–500 µL samples of whole culture suspension were collected and DNA was extracted using commercial kits  following the manufacturers’ instructions. Standard PCR amplification of fragments of the pan-bacterial 16S rRNA sequence and rotocols ,33. All 23 cells  for the eviously . Wolbacheviously . All qPCWolbachia MLST database on PubMLST [Wolbachia sp. isolated in this study with other existing Wolbachia sp. was inferred using the Bayesian Markov chain Monte Carlo in MrBayes 3.2 [Wolbachia MLST scheme [coxA, HKY+G for fbpA and GTR+G for the remaining genes.Pair-end sequences obtained from PCR amplicons were assembled to produce a corrected consensus using 4Peaks  and Clustal Omega . The seq PubMLST . The seq PubMLST . The phyayes 3.2 , with coayes 3.2 . The modWolbachia strain wStri bacteria, harvested from heavily-infected AeAl-2 cells  and infection level (from <10 to >50 bacteria per cell) over the subsequent 4 months. Then, during the following 6 weeks, the bacterial growth rate increased rapidly until around 50% of cells were infected with up to >100 bacteria visible per cell. At this point the first subculture onto fresh BME/CTVM23 cells was carried out; four weeks later this passage 1 culture was heavily infected and was passaged onto fresh BME/CTVM23 cells after a further 7 weeks. The passage 2 culture displayed a heavy infection between 8 and 12 weeks later . The Wolbachia wsp gene was also detectable by PCR in passages 1 and 2 at one month after subculture. The infected cells did not exhibit any cytopathic effects detectable by weekly inverted microscope examination. Intracellular bacteria were observed in Giemsa-stained cytocentrifuge smears prepared from the parent culture at eight months p.i., with approximately 25% of cells infected , MT577878 (coxA), MT577879 (fbpA), MT577880 (ftsZ), MT577881 (gatB) and MT577882 (hcpA), and compared with published sequences to determine its relationship with other Wolbachia strains.Seven pools of flea organs were inoculated into separate IDE8 cell cultures; of these, a single pool of five individual infected a,b. To fwCfeF isolate within the clade consisting of arthropod and filarial Wolbachia strains from the F supergroup (posterior probability = 100). These include two Wolbachia strains previously identified in Ctenocephalides felis from Georgia, USA [Wolbachia MLST scheme: fragments of the coxA, fbpA, ftsZ, hcpA and gatB genes [wCfeF with Wolbachia strains from the F clade, including those from insects such as Supella longipalpa (Blattodea), Paratrechina longicornis (Hymenoptera) and Cimex lectularius (Hemiptera)  a. Phylog F clade b–f. In tr clades . AltogetWolbachia can invade and replicate in tick cells, at least in vitro. We demonstrated susceptibility of three cell lines derived from I. scapularis and one each from I. ricinus and R. microplus. Two laboratory-cultured strains and one novel field strain of Wolbachia, originating from diverse insect groups  and belonging to different supergroups (B and F), were able to infect and grow in tick cells. Special procedures, such as the shell vial technique or centrifugation, were not necessary for the initial infection of tick cells with Wolbachia, and infected cultures were maintained for long periods without the need for special incubation conditions. As seen in chronically-infected mosquito cell cultures [Wolbachia strains wAlbB and wCfeF did not display any cytopathic effect over prolonged periods in vitro. In contrast, Ixodes spp. cultures heavily infected with the wStri strain displayed cytopathic effects, manifest as hypertrophied or pyknotic cells; the same strain in mosquito cells (AeAl-2) caused heavily infected cells to lose the ability to attach to a surface [It is clear from our results that cultures ,72, tick surface .Anaplasma [Cardinium [Ehrlichia [Neoehrlichia [Mycobacterium [Rickettsia [Spiroplasma [Wolbachia persica, in a Dermacentor albipictus tick cell line [Francisella [Rickettsia felis [Mycobacterium leprae, the causative agent of leprosy, transmission of which may be associated with biting insects and ticks [Wolbachia of insect origin. The ability of the novel Malaysian flea-derived Wolbachia isolate to infect and grow in a tick cell line confirmed the usefulness of tick cell lines in cultivating bacterial species of which ticks are not the natural hosts.Considered together, all the tick cell lines used in the present study have previously been shown to be permissive for a wide range of intracellular bacteria transmitted and/or harboured by invertebrates. These include representatives of the genera naplasma ,74, Cardardinium ,75, Ehrlhrlichia ,77,78, Nhrlichia ,80, Mycoacterium , Rickettckettsia ,82,83,84roplasma ,85. A prell line , but thincisella . Althougnd ticks ,88. Ticknd ticks . TherefoWolbachia has been detected by molecular techniques: I. scapularis [I. ricinus [R. microplus [Wolbachia DNA include the ixodid ticks Amblyomma americanum [Rhipicephalus sanguineus [Dermacentor silvarum [Haemaphysalis hystricis [Ornithodoros rietcorreai [Wolbachia and nematodes was not demonstrated [I. ricinus [Wolbachia and parasitic insects by screening samples for the presence of DNA from both bacteria and the parasitic wasp Ixodiphagus hookeri.The cell lines used in the present study were also chosen because they belong to species in which apularis ,44, I. r ricinus ,36,38,43icroplus ,41. Otheericanum , Rhipicenguineus , Dermacesilvarum  and Haemystricis  and the tcorreai . None ofnstrated . To date ricinus ,48 have Wolbachia strains, as revealed in Giemsa-stained smears, was quite similar to that of other members of the Rickettsiales propagated in tick cell lines, such as Anaplasma marginale, Ehrlichia ruminantium, Neoehrlichia mikurensis and Rickettsia raoultii [wAlbB strain in BME/CTVM23 cells was more difficult to interpret. Readily-identifiable bacteria, such as those seen in R. raoultii-infected cells of the same line [wMelPop strain of Wolbachia in D. melanogaster brain cells was reported previously [Wolbachia in B. malayi nematodes, immunogold labelling was used to show that vesicles found in association with Wolbachia were of bacterial origin [The intracellular morphology of the raoultii ,73,77,80ame line , were noeviously , and thel origin .Wolbachia strains  can be propagated continuously within insect cell culture systems, they belong exclusively to the more well-characterised A and B supergroups [Wolbachia strain is the only F supergroup member to be isolated into a cell line to date, the only flea-derived strain to be conclusively isolated and propagated over a prolonged period and the only Wolbachia field strain to be isolated into a tick cell line. A previous study did report the isolation of Wolbachia-like organisms from C. felis alongside Rickettsia felis in ISE6 cells; however, these putative Wolbachia were only identified morphologically and molecular confirmation was not presented [Although over a dozen ergroups ,72,90. Tresented .Wolbachia F supergroup is unique as members have been found in both arthropod and nematode hosts [Wolbachia (wCle) and its host, the bedbug Cimex lectularius [Wolbachia infection in a flea was a comparison between laboratory and wild populations of the gerbil flea, Synosternus cleopatrae, that detected a negative impact of infection on reproductive success in the laboratory colony but not in the wild population [C. felis genome from a laboratory colony in California led to the generation of two complete Wolbachia genomes, neither of which was placed in supergroup F [wCfeT, contains a complete biotin synthesis operon and may be involved in providing nutrients to the host cell. The second genome, wCfeJ, encodes a toxin-antidote system similar to that responsible for CI in dipteran hosts. However, experimental confirmation of mutualistic or parasitic roles for these Wolbachia in C. felis has not been published. The availability of a Wolbachia isolate from the F supergroup growing in a cell line will allow for further genetic and phenotypic characterisation at the cellular level, which could be useful in elucidating the Wolbachia-host relationship for this particular strain.The de hosts ,93. A prtularius . To the pulation . Interesrgroup F . One of C. felis genome project and the detection of Wolbachia DNA in Ctenocephalides spp. fleas in several other studies [Wolbachia bacteria had not been visualised or isolated from fleas of this genus prior to the present study, although immunohistochemistry and transmission electron microscopy identified Wolbachia in the sand flea T. penetrans [Ctenocephalides sp. fleas may not only host a wide range of Wolbachia strains from several distinct supergroups, but may also contain strains from a novel, divergent clade [Wolbachia from the B and F supergroups was detected in C. felis [wCfeF isolate. The phylogenies of 16S rRNA and the MLST scheme genes provided compelling evidence for the placement of the wCfeF isolate within the F supergroup, similar to the Wolbachia strains previously reported from North American C. felis [Despite the completion of the  studies ,22,24,26enetrans . Sequencnt clade ,22,26,93C. felis .Wolbachia from Malaysia and the wider Southeast Asian region have primarily concerned the A and B supergroup Wolbachia from mosquitoes, owing to national research priorities placed on mosquito-borne diseases [Wolbachia in other insects, such as the tephritid fruit flies and the butterfly Hypolimnas bolina, as well as filarial nematodes including Brugia pahangi and Onchocerca borneensis [Wolbachia strain from Ctenocephalides sp. fleas in this region.Reports of diseases ,96,97. Arneensis ,100,101.coxA, fbpA and ftsZ) showed that wCfeF appeared to be closely related to Wolbachia strains from the longhorn crazy ant P. longicornis, and the brown-banded cockroach S. longipalpa [Wolbachia between Myrmecophilus spp. ant crickets and their ant hosts, including P. longicornis [Wolbachia strains in C. felis are still not fully understood, although it has been suggested that the predation of fleas by other insect species could be a possible mode of transmission between distantly-related hosts [The phylogenies from the concatenated genes and a subset of individual MLST genes  being found in the region [C. orientis has a strong host preference for dogs and, to a lesser extent, small ruminants [wCfeF in Malaysia and the wider South-East Asian region.It is important to consider the possibility of there being more than one e region . Due to uminants ,105. TheWolbachia from naturally-infected arthropods. The ability to propagate Wolbachia continuously in tick cell lines provides a platform to examine how these bacteria might modulate the replication of tick-borne arboviruses, bacteria and protozoa, potentially leading to novel control strategies for tick-borne diseases. In light of our demonstration, that tick cells are capable of supporting long-term infection with Wolbachia, the exact nature of the relationship between the bacterium, ticks and other parasitic arthropods such as Ixodiphagus in the field should be re-examined.Tick cell lines can now be added to panels of insect, predominantly mosquito, cell lines to enhance the capability to isolate field strains of