Understanding how heart muscle cells function and developing new strategies for repairing the damaged heart are important challenges for tackling heart disease. Human cells from the heart are required in the laboratory for these investigations, but are difficult to obtain in large numbers from patients. As an alternative, stem cells can be used which can be cultured indefinitely in the laboratory and then turned into heart muscle cells. The current methods lead to a mixture of cell types, belonging to the different regions of the heart, creating difficulties when trying to understand the biology of a particular area of the heart, or for future applications when these stem cell-derived cell types are used to repair the heart. Genetic modification of stem cells provides a solution to this problem, as these techniques allow fluorescent markers, so-called reporters, to be inserted into key genes that are active in the different cell types. The different coloured reporters thus allow the identification and purification of specific cell types. In this review, we discuss the various methods that can be used to establish these reporter systems and highlight their applications in different aspects of cardiovascular medicine.Recent advances have made pluripotent stem cell (PSC)-derived cardiomyocytes an attractive option to model both normal and diseased cardiac function at the single-cell level. However, in vitro differentiation yields heterogeneous populations of cardiomyocytes and other cell types, potentially confounding phenotypic analyses. Fluorescent PSC-derived cardiomyocyte reporter systems allow specific cell lineages to be labelled, facilitating cell isolation for downstream applications including drug testing, disease modelling and cardiac regeneration. In this review, the different genetic strategies used to generate such reporter lines are presented with an emphasis on their relative technical advantages and disadvantages. Next, we explore how the fluorescent reporter lines have provided insights into cardiac development and cardiomyocyte physiology. Finally, we discuss how exciting new approaches using PSC-derived cardiomyocyte reporter lines are contributing to progress in cardiac cell therapy with respect to both graft adaptation and clinical safety. Pluripotent stem cells (PSCs), including embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs), have revolutionised the study of developmental biology and diseases ,2,3,4,5.Although primary adult cardiomyocytes (CMs) have provided important insights ,17, theiThe ability to label individual cell types non-invasively using a genetically encoded fluorescent reporter offers the prospect of greater insight into cardiac differentiation and the isolation of pure populations of specific cell types. In this review, we will discuss how such fluorescent reporter lines are generated and how reporter cells can be used to study developmental cardiology, CM function and how they have advanced cardiovascular medicine. In order to produce a fluorescent reporter line, a transgenic construct is required comprising a fluorescent protein (FP) inserted downstream of a promoter or gene of interest (GOI). A reporter line can be generated as a transient line, where the transgene remains episomal, or as a stable line, where the transgene becomes integrated into the host genome, either site-specifically or randomly. A summary of the various methods is presented in A transient reporter line is produced by introducing the reporter construct as a non-integrating plasmid or vector, e.g., adenovirus  into theThis method relies on the reporter construct being cloned, packaged and delivered via a genome-integrating viral system, e.g., a lentiviral vector  Figure B or in tThis technique is relatively simple but possesses many biological and technical risks attributable to the random insertion into the genome . FirstlyIn order to avoid the undesirable effects of a random integration, fluorescent reporter constructs can be inserted into specific genomic sites. Homologous recombination (HR) C 26] is is26] isThe development of site-specific endonucleases including zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) and clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR associated protein 9 (Cas9) has transformed our ability to manipulate the genome . These cAAVS1) [Gt(ROSA)26Sor [LoxP or Frt sites, are inserted into the SHS, and equivalent sites are incorporated into the reporter construct, enabling the sequences to be efficiently exchanged through the activity of recombinases [With respect to site-specific integration of a fluorescent reporter, there are two common strategies. In the first strategy, the reporter construct can be introduced into a so-called safe harbour site (SHS) E, e.g., AAVS1) , chemokiSA)26Sor . These gSA)26Sor  and the mbinases . This membinases ,34. A general disadvantage for all exogenous fluorescent reporter constructs is that a minimal promoter region is used. Subsequently, the fluorescence readout may not faithfully recapitulate the biological expression of the GOI. To avoid this issue, endogenous regulatory elements can be exploited for FP expression, allowing a higher fidelity between reporter and actual promoter activity. Using HR, FPs can be inserted downstream F of an eEach construct design has inherent advantages and disadvantages and the exact approach adopted should be tailored to the study. Random integration may be a sensible approach for studying gene function and pre-screening of biological effects. A master cell line is more suitable for lineage-specific labelling where multiple reporter lines are needed. Lastly, endogenous locus targeting is safer for projects that rely on the sensitivity of the reporter system. In vitro PSC-CM differentiation involves sequential stages of mesoderm induction, cardiac mesoderm determination, lineage specification and terminal differentiation . WNT/β-cBRY) which has been used to drive a GFP fluorescent reporter. In vitro differentiation of mouse ESCs (mESCs) revealed a multipotency of canonical WNT-activated Bry-GFP+ cells. Under cardiogenesis-promoting conditions, Bry-GFP+ cells with low expression of kinase insert domain receptor (Kdrlow) exhibit cardiogenic potential whilst, Bry-GFP+, Kdrhigh populations possess haematopoietic potential [In a developing mammalian embryo, the earliest cardiovascular progenitors are identified in gastrulation, where cells enter epithelial-mesenchymal transition and form mesendoderm. These progenitors express a mesodermal marker T-box transcription factor brachyury . Investigations which used a fluorescently-tagged MESP1-mCherry/ NK2 homeobox 5 (NKX2.5)-eGFP dual reporter hESCs [MESP1-mCherry expression followed by NKX2.5-eGFP expression. Enrichment of WNT5A, receptor tyrosine kinase-like orphan receptor 2 and frizzled class receptor 2 in MESP1-mCherry+ cells suggested roles of non-canonical WNT pathway in cardiac progenitor determination [The transition from common mesoderm towards cardiac mesoderm requires canonical WNT inhibition and non-canonical WNT activation. These intermediate cells are marked by an expression of mesoderm posterior basic helix-loop-helix transcription factor 1 (er hESCs  demonstrmination .ISL1) is a potential SHF marker [ISL1-cre dsRed reporter hESC line [+ multipotent status with differentiation potential for CMs, smooth muscle, endothelial and epicardial lineages. This finding may highlight a common ancestor shared between pro-epicardial progenitor and SHF CPCs since time-course analysis showed that the ISL1 expression preceded the expression of SHF markers T-box transcription factor 1 and NKX2.5. NKX2.5/ISL1 or ISL1/epicardial-specific Wilm’s tumour protein 1 dual reporter systems may be able to elucidate the diversification of ISL1+ progenitor and SHF CPCs further. More recently, CRISPR-Cas9-assisted generation of a NKX2.5-TagRFP/T-box transcription factor 5 (TBX5)-Clover dual reporter hiPSC line [NKX2.5-TagRFP+/TBX5-Clover+); presumptive SHF (the NKX2.5-TagRFP+/TBX5-Clover−); pro-epicardial cells (NKX2.5-TagRFP−/TBX5-Clover+), which diversified into nodal-like CMs and epicardial cells; and endothelial progenitors (NKX2.5-TagRFP−/TBX5-Clover−) (The nascent cardiac progenitors undergo another transitory stage towards more defined cardiac lineages termed the first heart field (FHF), which contributes to the left ventricle and a portion of the atria, and the second heart field (SHF), which gives rise to the right ventricle, atria, outflow tract and inflow tract . In the F marker . A studyESC line  ascertaiPSC line  has offeClover−) .Taken together, the stage-specific fluorescent PSC-CM reporter cell lines offer an optical tracking strategy for cell identity and enable in vitro studies of cardiac development. Uniquely, hiPSC-CM reporter cells provide insights into human cardiogenesis without embryo donor requirements and without the concerns over species differences encountered with mouse models. Future multi-coloured reporter lines  may enabDespite the refinement of in vitro cardiac induction protocols , PSC-CM Percoll gradient centrifugation  allowed MYH6) [TNNT2) [NKX2.5 [+/Ca2+ antiporter (NCX1) [2+ transient (CT) reduction and action potential (AP) shortening [Genetically engineered reporter lines provide a robust alternative. Reporter lines carrying an antibiotic-resistant cassette under the control of cardiac-specific gene’s regulatory elements, e.g., α-myosin heavy chain (MYH6) ,78,79,80 [TNNT2) , NKX2.5  [NKX2.5  and Na+/r (NCX1)  allow efr (NCX1) ,76. Howeortening . MeanwhiNKX2.5 is an early transcription factor in the FHF and SHF CPCs and plays supportive roles in cardiac contraction [NKX2.5-GFP reporter hESC line [NKX2.5 expression is also detectable in non-cardiac tissues such as the spleen, stomach and thyroid [NKX2.5 may be advantageous.traction . An engiESC line  allowed  thyroid ; hence, MYH6-GFP+[MYH6-mCherry+ [MYH6 reporter systems are better suited for the isolation of early committed human CMs and vice versa for mouse CMs. The ability of sarcomere-specific reporters to aid in isolation of beating CMs was also recently reported for a number of key genes: α-cardiac actin (ACTC)-mCherry [TNNT2-firefly luciferase /puromycin resistance [TTN)-GFP hPSC lines [Fluorescent reporter transgenes under the control of a sarcomere gene/promoter offer a more cardiac-specific cell labelling. Isolated fluorescent MYH6-GFP+,49 or MYmCherry+  hPSC-CMsmCherry+ ,85,86. T-mCherry , TNNT2-fsistance  and titiSC lines . In addiSC lines .NCX1 plays essential roles in intracellular Ca2+ flux and excitation-contraction coupling [NCX1-eGFP hPSC reporter line [NCX1-eGFP+ cells displayed both molecular and functional characteristics of typical hPSC-CMs.coupling . An NCX1ter line  offered Irrespective of the pan-cardiac promoter used in these reporter systems, FACS purification yielded a mixture of atrial, ventricular and nodal CM subtypes. These reporter lines benefit cardiotoxic screening and developmental studies, but the heterogeneous nature of the cell population complicates chamber-specific drug discovery, disease modelling and therapeutic applications. Each subtype exhibits unique physiological characteristics , leadingMYL2), is the most attractive ventricular marker with an expression restricted to ventricular compartments throughout mammalian development [MYL2 PSC-CM reporter lines [MYL2 is considered a late cardiac gene, where its expression level increases consistently with the stage of CM maturation, with the upregulation seen being more pronounced than other maturation factors, such as β myosin heavy chain [MYL2-FP+ cells is important to optimise the yield of ventricular-like cells.Myosin light chain 2  is ubiquitously expressed within the primitive heart tube and only becomes confined to atrial compartments at later developmental stages [MYL7-GFP hiPSC-CMs identified co-expression of MYL2 and MYL7 in 96% of the MYL7-GFP+ fraction [The selection of atrial-like cells is more challenging owing to the lack of a reliable genetic marker. Myosin light chain 7  expression is confined to atrial lineages [SLN-tdTomato+ hiPSC-CMs [SLN as an alternative atrial marker. Following this report, an SLN-driven voltage-sensitive fluorescent protein (SLN-VSFP) hiPSC reporter line was generated [SLN-based atrial cell purification has been emphasised due to the downregulation of the gene as the cells become more mature [During mammalian heart development, sarcolipin  enhancer activity restricted to the atrioventricular conduction system [cGATA6-eGFP hESC reporter line [HCN4).PSC-derived nodal-like CM enrichment is demanding due to this subtype being a relatively minor population under WNT-modulated differentiation procedures . Investin system . An engiter line  enabled SHOX2)-VSFP hiPSC line [HCN4 and nodal-type AP. SHOX2 promotes the transcription of HCN4 [SHOX2 was sufficient to induce endogenous SHOX2 expression and steered the differentiation of ESCs towards the nodal lineage [A transgenic short stature homeobox 2  for CT measurement is common, these chemicals are known to suppress cardiac functions [2+ reporters are being explored as we outline below.Electrophysiological measurements are not only a reliable strategy to identify individual CM subtypes, they also allow investigation of either genetically inherited or acquired functional cardiac defects. Patch-clamp electrophysiology is the gold standard AP recording technique , but it activity , which ttraction . Althougunctions  and prevunctions . GeneticCiona intestinalis and a super ecliptic pH-sensitive GFP variant (pHluorin A227D) [CAG-Arclight transgene into hiPSC-CMs enabled the ubiquitous expression of the GEVI driven by a synthetic CAG control element [Arclight reporter showed improved signal-to-noise ratios and was less affected by photobleaching than conventional dyes. To facilitate simultaneous subtype identification and electrophysiological studies, a GEVI can be directed by a CM subtype-specific promoter. As an alternative, VSFP-based GEVIs [Arclight system, provides a more stable AP pattern, unaffected by movement artefacts and photobleaching.The development of genetically encoded fluorescent voltage-sensitive indicators (GEVIs) has opened a new window for electrophysiological studies. Arclight is a synthetic protein containing a voltage-sensing domain from n A227D) ,104. Ran element . The flu element ,106. Fured GEVIs  rely on ed GEVIs  to track2+ indicators (GECIs) have been engineered and tested in PSC-CMs [2+-modulating protein (CaM), a CaM-interacting peptide, e.g., myosin light chain kinase-derived M13 and a circularly permuted GFP [2+-induced Ca2+ release, cytosolic Ca2+ binds to CaM which promotes the formation of Ca2+-CaM-M13, resulting in increased fluorescence [2+ dyes is the stable expression of the transgene, facilitating observations of the chronic effects of pharmacological molecules, and non-cytotoxic properties [Several versions of genetically encoded fluorescent Ca PSC-CMs ,65,106. uted GFP . Followirescence . Similaroperties . The fidoperties ,106.Chlamydomonas reinhardtii-derived channelrhodopsin 2 protein (ChR2) mediates inward cation currents [CAG-humanised ChR2 (hChR2)-mCherry reporter hiPSC-CMs could be optically paced at different beat rates [The field of optogenetics has captured interest in neurophysiology because of the ability to track spatiotemporal activation of neurons . Upon blcurrents , resulticurrents . Differeat rates , offerinat rates . Furtherat rates  allows aThe adult human heart has limited regenerative capacity to self-repair . hiPSC-CNkx2.5-GFP+ miPSCs confirmed the cardiac differentiation potential of the CPCs without teratoma formation [The proliferative capacity of stem cells is desirable for cardiac regeneration to compensate for the massive cell loss seen following transplantation . Howeverormation . Future MYL2-eGFP+ hPSC-CMs revealed signs of morphological maturation and connexin 43 (CX43) expression at the host–graft interface [Host-graft adaptation is another concern for cardiac cell therapy. Importantly, fluorescent PSC-CM reporters provide not only a tool for tracking the cell fate after transplantation but also provide information about graft–host structural and functional integration. Histological analysis of rat heart pre-intramyocardially injected with nterface . Since Cnterface , the preNotwithstanding, the immaturity of hPSC-CMs still represents a challenge for cardiac cell therapy. New methodologies focused on promoting more adult phenotypes are needed to ensure clinical safety and maximize therapeutic benefit.2+ fluxes and β-adrenergic response [2+ cycling and mitochondrial respiratory capacity while promoting sarcomere organisation [Several techniques have been developed to improve iPSC-CM maturity including biochemical, physical and environmental stimulation . Modulatresponse . Similarnisation .Unlike PSC-CMs, primary adult atrial and ventricular CMs do not beat spontaneously, but require an electrical signal from nodal CMs. Mimicking the in vivo situation, extrinsic electrical or optical (for optogenetics) impulses not only help to pace cultured CMs but also exert positive effects on the ultrastructural organisation and functional properties ,125.Extracellular matrix stiffness changes dramatically during cardiac development and has an impact on the mechanical force experienced by CMs . 3-dimenCX43 [Combinations of 3D hiPSC-CMs with biochemical  or electCX43  may be bThe indefinite source of PSC-CMs has the potential to provide scalable CM production for cardiovascular research. However, immature and embryonic-like phenotypes as well as the heterogeneous nature of PCS-CMs present challenges for their use in drug discovery, disease modelling and transplantation. Stage-specific fluorescent PSC-CM lines generated by different transgenic methods have helped researchers monitor cardiac development and improved in vitro differentiation protocols. The discovery of additional CM maturation-promoting pathways will require multi-coloured reporter systems in combination with advances in 3D PSC-CM culture.PITX2 and KCNA5, in combination with atrial-promoting differentiation approaches including retinoic acid treatment and CRISPRa/CRISPRi-mediated transcriptional activation/inhibition of atrial-regulating pathways [CM surface markers are largely unknown and thus FACS-mediated PSC-CMs sorting by fluorescent markers provide a genetic-based solution for specific cell-type isolation. Purified fluorescent PSC-derived CPCs have been successfully differentiated into functional CMs in vivo, devoid of tumorigenic risk, promising clinical safety for cardiac regeneration. Novel fluorescent PSC reporter lines will help identify the various CPCs that give rise to unique cell-type compositions specifically required for treating different pathological conditions. Furthermore, the isolation of subtype-specific PSC-CMs will enable subtype-specific cardiogenic drug discovery and disease modelling. Unlike nodal and ventricular types, the atrial PSC-CM markers are not fully characterised. Further insights from novel fluorescent atrial PSC-CM reporter lines, such as pathways ,137, couLastly, optogenetics is an emerging biotechnology that has recently been applied in PSC-CMs for spatiotemporal CM activation and maturation. The combination of optogenetics with fluorescent physiological reporters allows an all-optical measurement of CM function that benefits high throughput drug screening. We look forward to seeing the implementation of combined optogenetics and physiological sensors with minimised optical crosstalk in cell transplantation, which will allow PSC-CM graft performance to be followed in vivo.


Y877). HER2 and pHER2Y877 status were evaluated in a cohort of BC patients representative of molecular subtypes distribution (n = 497) and in a series of BC cell lines (n = 7). Immunohistochemistry against pHER2Y877 was performed on tissue micro arrays. Cellular proliferation assays were performed on BC cell lines presenting different combinations of HER2 and pHER2Y877 status and treated with increasing doses of trastuzumab (0–150 μg/ml). The prevalence of pHER2Y877 in this cohort was 6%. Nearly 5% of patients with HER2-negative tumors  overexpressed pHER2Y877. Among triple negative BC patients , 7.7% expressed pHER2Y877. Trastuzumab treatment decreased cell proliferation in HER2−/pHER2Y877+ BC cell lines, to an extent comparable to what occurs in HER2+ cell lines, but did not affect HER2−/pHER2Y877− cell lines. Trastuzumab sensitivity in HER2−/pHER2Y877+ cell line is specific to HER2 tyrosine 877 phosphorylation. Hence, with further confirmation in a bigger cohort, trastuzumab treatment could be envisaged as a treatment option to women presenting with HER2−/pHER2+ tumors, representing more than 1000 BC women in Canada in 2019.The breast cancer (BC) biomarker HER2  is overexpressed in 25% of BC. Only patients with HER2-positive tumors receive HER2-targeting therapies, like trastuzumab (Herceptin). However, some women with a HER2-negative BC could benefit from trastuzumab. This could be explained by the activation/phosphorylation of HER2 that can be recognized by trastuzumab. The aim of this study is to examine trastuzumab effects on HER2 phosphorylation at tyrosine Y877 (pHER2 According to the Public Health Agency of Canada (PHAC), breast cancer (BC) represents 25% of new cases of cancers and 13% of all cancer deaths in women in 2019. Although statistics are provided for one disease, BC can be classified into four molecular subtypes depending on the overexpression of three molecular markers: estrogen receptor (ER), progesterone receptor (PR), and Human Epidermal Receptor 2 (HER2) . Fo. FoY877 Y877 combination status. Cells were plated on 96-well plates in triplicate . After 24 hours of incubation, trastuzumab (Roche-Genentech) was diluted in miliQ water (1 mg/ml) and added to cells at increasing concentrations . After 5 days of incubation, alamarBlue was added . Viability rate was determined by fluorescence. Percent viability was calculated for the treated cells compared to the untreated cells. All experiments were done in triplicate and mean ± SD of triplicates was calculated and plotted for each drug concentration.Seven cell lines were selected according to their HER2/pHER2All figures were generated using Prism/Graphpad (version 5). A two-way ANOVA test with an interaction term was used to examine differences between cell lines response to trastuzumab using the SAS software (version 9.4).As shown in Y877 clinical relevance, pHER2Y877 distribution was established in the BC population, as measured by the HER2Y877 prevalence in the BC patient cohort. As shown in To evaluate pHER2Y877 expression was scored as 0 (no staining is observed or membrane staining is observed in <10% of the tumor cells), 1+ (a faint perceptible membrane staining is detected in >10% of tumor cells with incomplete membrane staining), 2+ (a weak to moderate complete membrane staining is observed in >10% of tumor cells) and 3+ (a strong complete membrane staining is observed in >10% of tumor cells), as described in the scoring guidelines of the 2013 ASCO/CAP.As depicted in Y877 prevalence was first established in our cohort of BC patients. As mentioned in Y877. Among them, the distribution of HER2 status and molecular subtypes were representative of what is observed in the BC patient population at large  [Y877 is well distributed among BC molecular subtypes and HER2 status, and is not specific to a subgroup in particular.The pHER2minal A) . This inY877 staining according to HER2 status and molecular BC subtypes. HER2-negative BC represented 82% of the cohort; among them, 4.9% were pHER2Y877+. HER2+ and HER2-equivocal represented both 9% of the cohort; among them, 17.4% and 4.4% were pHER2Y877+, respectively , 11.8% were pHER2Y877+. Finally among Luminal B (9%), 4.8% were pHER2Y877+  and PR (Progesterone Receptor) were confirmed by literature. HER2 , ER (Estrogen Receptor), PR (Progesteron Receptor), TNBC (Triple Negatif Breast Cancer).Breast cancer cell lines selected depending on their HER2 and pHER2Y877+ BC cell lines BT-474 and SKBR3 (p<0.0001 for both), as there is no difference between either the cell lines (p = 0.11) or the slopes (p = 0.99) (The JIMT-1 BC cell line was used as control as it is a known trastuzumab-resistant cell line . This wa = 0.99) . This deY877− BC cell line, as a decrease in proliferation can be observed  and the HER2+/pHER2Y877+ cell lines but the decrease of proliferation is less important in MDA-MB-453 (HER2+/pHER2Y877−), indicating an added value of a phosphorylated Y877 in response to trastuzumab.Hence, trastuzumab acts similarly on MDA-MB-453  is sensitive to trastuzumab, but at higher doses  proliferation  is related to pHER2Y877 overexpression.Taken together, these results suggest that the decrease in proliferation induced by trastuzumab observed in the TNBC cell line MDA-MB-468  [Y877+ in our 497 BC patients cohort. Taken together and as pictured in Studies have evaluated the pHER2 prevalence at Y1248 and Y1221/1222 in BC patients cohorts, and among HER2− BC patients . The prevalence of pHER2 at these positions was 0–38% and 10–15%, respectively . In the R2Y1248− . In anottaining) . This isY877+. Although a decrease in proliferation is observed, it is not significantly different from MDA-MD-231 (HER2+/pHER2Y877−). This could also be the reflection of inter-individual response to trastuzumab treatment, similar to what is observed in BC patients. Worth mentioning is that TNBC can be classified into seven subtypes, such as Basal-like 1 (BL1), Basal-like 2 (BL2), Immunomodulatory (IM), Mesenchymal (M), Mesenchymal stem-like (MSL), Luminal androgen receptor (LAR) and Unstable (UNS). It has been shown that TNBC subtypes have different therapeutic responses [et al. have reported that BL1 subtype has the highest pathologic complete response rate to neoadjuvant chemotherapy compared to the other TNBC subtypes [However, trastuzumab sensitivity differs slightly for the TNBC cell line BT-549, which is also HER2−/pHER2esponses . Masuda subtypes . MDA-MB-subtypes . This diY877 status. However, these cell lines also have other molecular characteristics. Multiple cell cycle proteins  and survival signaling pathways (PTEN and PIK3CA) are involved in BC and can affect the response to treatment [Y877+) have a loss of RB1 and PTEN compared with the other cell lines, but they are still sensitive to trastuzumab. MDA-MB-231 (HER2−/pHER2Y877−) has the same molecular characteristics for RB1, p53, PTEN, PI3KCA, BRCA1 and BRCA2 as SKBR3 and MDA-MB-453, which are both sensitive to trastuzumab . Guidelines are enacted by the American Society of Clinical Oncology/College of American Pathologist. 0 = No staining is observed, or membrane staining is observed in less than 10% of tumor cells. 1+ = A faint/barely perceptible incomplete membrane staining is detected in more than 10% of tumor cells. 2+ = A weak to moderate complete membrane staining is observed in more than 10% of tumor cells. 3+ = A strong complete membrane staining is observed in more than 10% of tumor cells. The scoring defines a HER2 status is considered negative for scores 0 and 1+, equivocal for a score of 2+ and positive for score 3+.Adapted from the HercepTest(TIF)Click here for additional data file.S2 FigY877 status was evaluated using the 2013 ASCO/CAP scoring guidelines after staining by IHC with anti-pHER2Y877 antibody. A score of 2+ pHER2Y877 staining were considered positive. HER2 status was evaluated using the 2013 ASCO/CAP guidelines after staining by FISH (Fluorescence In Situ Hybridization). Molecular subtypes were identified using the ER and PR status evaluated by IHC and the global HER2 status (IHC + FISH status). A) pHER2Y877 prevalence in the cohort. B) pHER2Y877 distribution according to HER2 status, defined by FISH.pHER2(TIF)Click here for additional data file.S3 FigY877 status was evaluated using the 2013 ASCO/CAP scoring guidelines after staining by IHC with anti-pHER2Y877 antibody. A score of 2+ pHER2Y877 staining was considered positive. HER2 status was evaluated using the 2013 ASCO/CAP guidelines after staining by FISH and IHC. Molecular subtypes were identified using the ER and PR status evaluated by IHC and the global HER2 status (IHC + FISH status). A) pHER2Y877 prevalence in the cohort. B) pHER2Y877 distribution according to HER2 status, defined by IHC+FISH.pHER2(TIF)Click here for additional data file.S4 FigY877 was performed by IHC with a specific anti-pHER2Y877 antibody (right column). BT-20: HER2-negative; pHER2Y877-positive. MCF7: HER2-negative; pHER2Y877-negative. SUM-149: HER2-negative; pHER2Y877-negative. SUM-159: HER2-negative; pHER2Y877-negative. ZR-75-1: HER2-equivocal; pHER2Y877-negative.HER2 status was obtained using the HerceptestTM kit–Dako (left column), while pHER2(TIF)Click here for additional data file.S5 FigEach cell line was treated with increasing doses of trastuzumab . The cell growth was calculated as the percentage of treated cells compared to untreated cells. All experiments were done in triplicates and means ± SD were calculated and plotted for each drug concentration. ANOVA has been done using SAS software. A) Comparison of BT-474 and SKBR3 to MDA-MB-453. B) Comparison of MDA-MB-468 and BT-549 to JIMT-1 and MDA-MB-231. C) Comparison of MDA-MB-468 and BT549 to MDA-MB-453. * p < 0.0001 between cell lines.(TIF)Click here for additional data file.S1 TableProtein expression and gene mutation in breast cancer cell line according to their molecular subtype. +: protein expressed; -: protein not expressed. mut: gene mutated; wt: gene wild-type.(TIFF)Click here for additional data file.


R. P. Kincaid, Y. Chen, and C. S. Sullivan would like to note the following. In follow-up confirmatory studies, we have attempted to repeat the work presented in Fig. 5B. We are unable to locate the original cell line reagents used in this work. We therefore attempted to regenerate similar cell lines to repeat this work. We attempted to generate stable cell lines in the GM19240 B-cell background, but we have repeatedly failed to generate these stable cell lines. These particular LCL cells appear to be refractory to efficient lentiviral transduction, and therefore, we conclude that the data forming the foundation of the original interferon-suppression-of-growth assay  are not reliable. Thus, although we have confirmed that miR-S6S7 can modulate effects of type I interferon on an ISRE reporter in cells, we conclude that we have no reliable evidence regarding the activity of miR-S6S7 on cell growth.Volume 5, no. 2, e00074-14, 2014, However, major conclusions from the original manuscript remain valid. These include the following: (i) simian foamy viruses (SFVs) encode miRNAs, (ii) SFV miRNAs arise via a noncanonical mode of biogenesis, and (iii) two SFV miRNAs share sequence identity and functionality in regulating target transcripts in common with the lymphoproliferative and immunosuppressive host miRNAs miR-155 and miR-132.We apologize for and deeply regret the errors in this publication. C. S. Sullivan, the principal investigator of the Sullivan lab and senior author, accepts full responsibility for allowing these errors to be included in the original paper.


CircTNFRSF21 was translated crossing the backsplice site in two different reading frames. Overexpression of circPVT1, circERBB2, circHIPK3, circCCNB1, circSMAD2, circTNFRSF21 and circKIF5B significantly increased colony formation. In conclusion, our data provide a comprehensive map of circRNA expression in lung cancer cells and global patterns of circRNA production as a useful resource for future research into lung cancer circRNAs.The class of circular RNA (circRNA) is characterized by head-to-tail bonds between exons formed by backsplicing. Here, we provide a resource of circRNA expression in a comprehensive panel of 60 lung cancer and non-transformed cell lines (FL3C dataset). RNA sequencing after depletion of ribosomal RNA quantified the expression of circRNA and linear RNA. We detected 148,811 circular RNAs quantified by 2.8 million backsplicing reads originating from 12,251 genes. The number of identified circRNAs was markedly higher using rRNA depletion compared to public polyA-enriched RNA-seq datasets. CircRNAs almost never started in the first exon nor ended in the last exon and started more frequently in earlier exons. Most circRNAs showed high cell line specificity and correlated positively with their linear RNA counterpart. Known cancer genes produced more circRNAs than non-cancer genes. Subsets of circRNAs correlated with cell proliferation, histological subtype or genotype.  Circular RNAs (circRNAs) were first described in eukaryotes in the early 1990s and were originally considered to be formed by splicing defects ,3. NowadBacksplicing is dependent on the splicing machinery and uses canonical splice sites ,6,7,8,9.circβ-catenin protects full-length β-catenin from phosphorylation by GSK3β and subsequent degradation [circFOXO3 RNA forms a complex with CDK2 and p21 to prevent cell cycle entry [Currently, the majority of studies linking circRNAs to cancer focus on their function as miRNA sponges ,24, thusradation . Finallyle entry .MALAT1 [PVT1 [linc00673 [Lung cancer, representing 11.8% of all cancer diagnoses, is the most commonly diagnosed cancer type worldwide . It is aMALAT1 , PVT1 [3T1 [PVT1  or linc0inc00673 .Due to their high stability, circRNAs are considered as good candidates for new biomarkers . A speciHere, we describe the circRNA landscape in non-small cell lung cancer cell lines. After assembling a platform of 60 lung cell lines (57 lung cancer cell lines and 3 non-transformed lung cell lines), we used deep sequencing of rRNA-depleted RNA for profiling the exonic circRNAs and the linear RNA transcriptome. We describe the general characteristics of this dataset taking into account differences between the gene level  and the backsplice level . Furthermore, we link circRNAs to specific phenotypes and genotypes in non-small cell lung cancer.n = 175 with two or three replicates per cell line) and mapped to a reference genome to generate the linear RNA dataset. Next, we identified circRNAs by identifying reverse mapped reads resulting from backsplicing and constructed a separate circRNA dataset. In total, we found 2.8 million backsplicing reads compared to 3.8 billion reads mapping linearly to the genome.We assembled a lung cell line panel of 60 lung cell lines, consisting of 50 adenocarcinoma cell lines, seven other NSCLC cell lines and three non-transformed cell lines , which wOverall, we found on average 731 circRNA reads per million reads in our dataset based on rRNA depletion prior to RNA sequencing. At the gene level, we detected circRNAs for 12,251 genes and provide the full dataset for 60 cell lines in We compared our dataset to a publically available dataset of the Cancer Cell Line Encyclopedia (CCLE) ,41 from Next, we looked at the enrichment in polyA stretches between the CCLE and the FL3C datasets. In the CCLE dataset, 11,441 circRNAs were identified, of which 5587 were overlapping with the FL3C dataset, which contained in total 148,811 circRNAs. When we compared the top 100 most strongly expressed circRNAs, 15 showed no overlap and 85 were shared between the datasets. Of the shared circRNAs, 69% contained polyA stretches of 5 or more consecutive As, versus only 33% of the circRNAs that were uniquely identified in the FL3C dataset. In conclusion, there may be a trend towards enrichment in polyA stretches in circRNAs that were identified by sequencing after polyA selection. In brief, efficient circRNA detection was feasible after rRNA depletion and subsequent RNA sequencing while the circRNA content detected in polyA-enriched RNA was significantly lower.BIRC6. Next, we repeated the analysis only for the circRNAs that were expressed in at least 30 out of the 60 cell lines focusing on more broadly detectable circRNAs. This filter resulted in 4124 circRNAs at the gene level and 5232 circRNAs at the backsplice level. In this subset, we found on average two and at median only one circRNA per gene with PTK2 expressing the maximum number of circRNAs with 28 unique backsplice sites. For 1170 genes, more than one circRNA was expressed in the majority of cell lines.After quantifying the linear and circular RNA read counts in the FL3C dataset a, we calCYP24A1  and ASPH .The normalized circular read counts  at the gene level ranged from 14,487 to 0.17, with an average of 70.51 circular reads at gene level in all cell lines c. The gecircCYP24A1 with a backsplice site linking exons 11 to 3 having 14417 counts and circRNA circASPH (backsplice site: exon 3-2) having 10,493 counts. Overall, the normalized counts at the backsplice level ranged from 14,417 to 0.17, with an average of 5.8 reads per unique circRNA.This result is mirrored at the backsplice level d, with tWhen comparing the expression of circRNAs across all cell lines tested, there were major differences between the analysis at the gene level e and thePVT1 having the largest circular fraction of its total RNA and CCNB1 having the lowest circular fraction of its total RNA  to the FPKM of the linear reads . When considering the average ratio of circRNA/total RNA over the 60 cell lines, the median ratio was 0.03 at the gene level versus <0.01 at the backsplice level.MUC16 indicating that circRNAs can have largely varying ratios to the total RNAs derived from the same locus. These values may be used to select highly efficiently circularized transcripts.Overall, the circRNA/total RNA ratios showed a broad distribution with a range from <0.01 to >23 for EFCAB12  gene, but this circRNA was only expressed in 5/60 cell lines. The second highest ratio of 427.8 was reached by the OTUD7A (OTU Deubiquitinase 7A) gene, which was expressed in 10/60 cell lines. When a cut-off of expression in ≥30 cell lines was used, MUC16 showed the highest ratio of 248.8 with an expression in 40 cell lines. Focusing on individual circRNAs at the backsplice level, OTUD7A (backsplice site: 4–3) had the highest circRNA/total RNA ratio of 427.8 but was only expressed in three cell lines. MUC16 (backsplice site: 69–68) was in the top 10 with a ratio of 248.8 and was expressed in 27 cell lines.When comparing the ratios in individual cell lines, the highest ratio of 561.3 at gene level was identified for the In summary, circRNAs showed a positive correlation with the total linear RNA derived from the same gene, but the ratios varied largely when comparing individual cell lines, genes or circRNAs.T-test p = 1.8 × 10−5). At the backsplice level, even 8.4% of circRNAs originated from cancer genes  of COSMIC (version 90) . At the er genes b. In conCircRNAs with a low circRNA/total RNA ratio were less frequently originating from cancer genes c. In sumWe determined the correlation of circRNAs with the proliferation rate of the FL3C cell lines, which we had experimentally determined before see . At the For circRNAs expressed in ≥30 cell lines, the correlation ranged from +0.52 to −0.45 c. The maAt the backsplice level, the correlation ranged from +0.65 to −0.45 for the whole dataset b, and +0As an orthogonal analysis to the correlation analysis, we divided the cell lines into two groups based on their proliferation rate. Cell lines with ≥5-fold increase in cell count within 72 h were considered to be fast proliferating (11 cell lines). Cell lines with ≤3-fold cell count increase within 72 h were considered as slowly proliferating (21 cell lines). At the gene level e, circRNNext, we selected circRNAs derived from CGC genes which were expressed in ≥30 cell lines and showed a strong positive and/or negative correlation between circRNA expression and proliferation.SMAD2, MET and DEK. SMAD2 and MET showed a positive correlation between proliferation and circRNA expression  and between proliferation and total RNA expression . In contrast, DEK showed a negative correlation between its circRNA expression and proliferation (−0.34), while there was notably no correlation between its total (linear) RNA expression and proliferation (−0.01).At the gene level, three genes drew our attention: circSMAD2 were identified with a high correlation to proliferation: backsplice site exon 6–exon 2 (+0.37) and exon 6–exon 3 (+0.35). MET, (backsplice site exon 2–exon 2) showed a correlation of +0.32 with proliferation. For DEK, also two circRNAs were identified with a large negative correlation to proliferation: backsplice site exon 9–exon 8 (−0.32) and exon 9–exon 3 (−0.31). For the KIF5B gene, two different circRNAs showed opposite correlations to proliferation: backsplice site exon 24–exon 20 (+0.29) and backsplice site exon 20–exon 18 (−0.29), while the total RNA showed a weaker negative correlation to proliferation of −0.16.At the backsplice level, two isoforms of CircSMAD2 (backsplice site exon 6–exon 2) was upregulated in fast proliferating cell lines and showed a significant 8.1-fold change, versus a significant 1.6-fold change for the total SMAD2 RNA. The stronger differential expression of circSMAD2-6,2 than the total SMAD2 mRNA was also verified by RT-qPCR (circSMAD2 (backsplice site exon 6–exon 3) resulting in a 2.5-fold change versus a 1.4-fold for the total RNA. For the circDEK isoforms, backsplice site exon 9–exon 8 was most strongly downregulated in fast proliferating cell lines, with a significant 6.3-fold change versus a significant only 1.1-fold change for the total RNA. CircDEK backsplice site exon 9–exon 3 had a significant 2.4-fold change versus a non-significant 1.1-fold change for the total RNA. CircMET showed a significantly higher expression in fast proliferating cell lines with a 4.8-fold change versus a significant only 1.4-fold change for the total RNA , the difference in circRNA expression of 128 genes reached statistical significance after correction for multiple testing, e.g., TNFRSF21 a.TALDO1, TMC5, MVP and FNDC3B, this change in circRNA expression was significant. Lastly, we grouped the tumor cell lines according to their genotype as determined in the COSMIC cell line project [EGFR  and 4996-fold for circCCNB1 (backsplice site 7-6) (circDEK (backsplice 9–3) increasing 1083-fold whereas total DEK-9,3 only increased 4-fold leading to a circRNA/total RNA fold change ratio of 255. At the other end, HIPK3-2,2 showed the lowest ratio with 290-fold change for the circRNA vs. 208-fold change for the total RNA, resulting in an overall increase in the circular to total ratio of only 1.4. Due to the high variability in initial circRNA levels between the respective candidates and the different overexpression properties, the proportions of circRNA relative to total RNA after overexpression differed substantially among the circRNAs ranging between 6% for circDEK (backsplice site 9–8) and 86% for circPVT1 (backsplice site 2–2)  c. For alite 7-6) d. The raite 2–2) e.R = −0,38) between the endogenous circRNA/total RNA read count ratio and the circRNA/total RNA ratio change upon overexpression. This indicated that for circRNAs which were present in a high fraction of the total RNA already endogenously, also the overexpression of circRNA and total RNA remained rather parallel . For circRNAs which made up only a very small fraction of the endogenous total RNA, the overexpression largely increased the fraction of circRNA relative to total RNA .Next, we analyzed whether the extent of the circRNA overexpression ratio d correlaOverall, we successfully constructed vectors that efficiently and specifically overexpressed circRNAs at base-pair resolution.circTNFRSF21. We validated the expression of circTNFRSF21 backsplice site linking exon 2 to exon 3 (2–3) by PCR  by PCR a,b.TNFRSF21 RNA (linear and circular) and Cyclophilin A RNA expression levels  (circERBB2 (backsplice site 11–7) which emanated from a known oncogenic driver gene in lung cancer [We started with the overexpression of ite 2–2) a, which ite 2–2) a. Furtheg cancer  led to acircHIPK3 which was highly expressed and showed a very high ratio of circular/total RNA expression. Overexpression of circHIPK3 (backsplice site 2–2) also significantly increased colony formation (circCCNB1 (backsplice site 7–6) was derived from a gene involved in cell cycle control [circTNFRSF21 (backsplice site 3–2), which was differentially expressed in tumor vs. non-transformed cell lines (As an example of a gene that had not been connected to cancer before (non-CGC gene), we selected ormation c. Since  control , we selell lines a, we obsll lines e.CircKIF5B (backsplice site 24–20) showed a positive correlation of 0.29 with proliferation. In the colony formation assay, the overexpression of circKIF5B (backsplice site 24–20) caused a significant increase  and at the backsplice level . Strikingly, 14.1% of the genes formed circRNAs in ≥90% of the 60 cell lines, whereas only 0.7% of individual circRNAs were found in ≥90% of the cells. A similar pattern emerges from the analysis of the correlation between the circRNA relative to the total (linear) reads from the same locus: the correlation was much higher at the gene level (median 0.27) than at the backsplice level (median 0.06). This observation could point to a hypothesis that in general, the formation of circRNAs from a gene is more “important” and hence more homogeneous and more frequently correlated than forming a specific circRNA from the respective gene―which may be subject to future studies to unravel the functional importance of different circRNAs produced from the same locus.Our data also illustrates a high cell line specificity for most circRNAs. This is corroborated by a comparison with MiOncoCirc , which rWhen looking at the acceptor and donor exons, we noticed that the first and last exons were almost excluded from circRNA formation in comparison to the second and second-to-last exons, respectively. The predominance of the second exon as an acceptor exon is in accordance with previous data by Ragan et al. . MechaniBIRC6 showed the highest number of circRNA isoforms in the FL3C dataset (418), which was in agreement with MiOncoCirc reporting more than 500 isoforms of circBIRC6 [CYP24A1, which has also previously been identified as the most highly expressed circRNA in the single A549 cell line [Focusing on the number of circRNAs originating from a single gene, ircBIRC6 . The genell line .Unsupervised hierarchical clustering of the top 100 most highly expressed circRNAs both at the gene level and at the backsplice level resulted in a separate cluster for the non-transformed cell lines, whereas this separation was less prominent between adenocarcinoma and non-adenocarcinoma NSCLC cell lines. Hence, circRNA expression levels could distinguish different cell types, which is in agreement with a previous study in hematopoietic cells .The overall correlation between circRNA expression and total (linear) RNA expression was neutral or positive, with a stronger positive correlation at the gene level than at the backsplice level, while negative correlations were very rare and only weak. Given that the linear primary RNA transcript is the precursor of both the mature mRNA and the circRNA, this trend to a positive correlation may not be unexpected. Several previous studies reported a lack of a significant correlation between circular and linear RNA. This difference may be explained largely by multiple reasons: first, several of these studies compared circular and linear RNA ratios in different heterogeneous tissues or different developmental stages ,53, whilThe differences in correlation for the different circRNAs from the same parental mRNA might be explained by the different elongation rates of RNA polymerase II (reviewed in ). The prThe known cancer genes (CGC)  producedWhen correlating circRNA expression to the proliferation rate of the cell lines, there was a larger subset of circRNAs that were only expressed in the slow and a smaller subset of circRNAs that were only expressed in fast proliferating cells. This result was in accordance with previous reports ,16 and wT-test p = 0.0014). Among the genes with a significant change in circRNA expression, some had been previously identified as markers for LUAD: TALDO1 [TMC5 [MVP [FNDC3B [TP53 wild-type versus TP53 mutant cell lines, there were two circRNA-producing genes that showed a significant change: SBDS and GBF1, with SBDS having previously been linked to TP53 [Adenocarcinoma cell lines showed a higher expression of circRNAs in comparison with non-adenocarcinoma NSCLC cell lines. This may in part be due to a difference in the average proliferation rate of the cells . The sev: TALDO1 , TMC5 [5O1 [TMC5 , MVP [59 [FNDC3B . This co to TP53 .circTNFRSF21. Overexpression of this circRNA gives rise to an additional band matching in size to an open reading frame (ORF) in circTNFRSF21 which crosses the backsplice site twice in two different reading frames. The translation of circRNAs has been described previously [circTNFSF21 crossing the backsplice site twice. Further research is warranted to find out the translation initiation mechanism of circTNFRSF21 and its functional role in lung cancer.Comparing tumor cells to non-transformed cell lines identified a number of significantly differentially expressed circRNAs―including eviously . Given teviously  or m6A meviously . Eukaryoeviously , which iLaccase2 introns form Drosophila melanogaster. The base-pairing between the intronic sequences brings the donor and acceptor exon into close proximity to each other, thus creating favorable conditions for circularization [We showed that overexpression of circRNAs is possible, using an adapted method that was originally described by Kramer et al. , using trization ,66. Howerization .circPVT1, which corroborates previous studies [Overexpression of circRNAs allowed us to study their phenotype and influence on colony formation. First, circRNAs originating from known cancer genes (CGC-listed genes) also showed an oncogenic effect and their overexpression in low endogenously expressing cell lines led to increased colony formation, e.g., for  studies ,46. SecocircTNFRSF21 being translated with two reading frames across the backsplice site. For the future, our resource can be widely used to identify new circRNAs in lung cancer, to find cell lines with endogenous expression of specific circRNAs for further experiments, to search for genes with a particularly high or low circularization rate, to select circRNAs correlated with cell proliferation for mechanistic studies or to identify circRNAs associated with certain genotypes in lung adenocarcinoma.In summary, our study provides a deep insight into the expression of circRNAs in lung cell lines. Next to our findings on global circRNA properties like correlation with linear RNA, exon distribution, distinction between non-transformed and tumor cell lines by PCA, enrichment in cancer genes and association with cell proliferation and genotypes, we provide individual examples of circRNAs governing the colony formation potential of lung cancer cells as well as 2 in cell culture dishes  [All cell lines were purchased from American Type Culture Collection  and were maintained in ATCC-modified RPMI-1640 medium  at 37 °C and 5% COzerland) . At maxizerland) .The proliferative capacity of the cell lines was determined as follows: 150,000 cells per well were seeded into a 6-well plate and trypsinized after 24 h, 48 h and 72 h, respectively (2-wells per timepoint). At each timepoint, the cells were counted in duplicate. Ratios between the starting amount of cells  and cell count after 24, 48 and 72 h were determined. The increase in ratios represented the proliferative capacity of the cell line and is provided in RNA was isolated with the RNeasy mini kit . An on-column DNase I digest was performed with the DNase I RNase-free DNase set (Qiagen). From 5 µg total RNA, ribosomal RNA (rRNA) was depleted with the RiboZero Gold Kit H/R/M . The RNA library was constructed with the SureSelect Strand-Specific RNA library prep for the Illumina Multiplexed Sequencing Protocol (Illumina) and sequenced with the HiSeq4000 paired end 100 bp kit (Illumina). All kits were used according to manufacturers’ instructions.Raw reads were mapped using Tophat2 ,69 with The number of reads mapping to the backsplice side were used to calculate the circRNA expression level, while the sum of the total linear reads mapping to the parental gene was used to measure the total (linear) RNA expression level. The circular/total ratio was calculated by dividing the normalized backsplice reads  by the linear FPKM. All statistical analyses were performed in R and the R-packages described (v3.6.0) .Cells were washed with 1x PBS  and lysed in Tri-reagent (Sigma-Aldrich). RNA was isolated according to the manufacturer’s instructions. DNA was digested with DNase I  for 30 min at 37 °C. Reverse transcription was performed on 1 µg of RNA with RevertAid Reverse Transcriptase  and random hexamer primers (ThermoFisher).cDNA was amplified using the Q5 high fidelity polymerase  following the manufacturer’s instructions according to the following program: an initial incubation of 30 s at 98 °C, followed by 30 cycles of 10 s 98 °C, 30 s 58 °C and 45 s 72 °C, followed by a final incubation of 5 min at 72 °C in a Bio-Rad T100 Thermal Cycler. Next, the PCR-reaction was mixed with DNA loading dye (New England Biolabs) and separated on a 1% or 2% agarose gel containing SybrSafe (Thermo Fisher) and run in 1× TAE . The gel was imaged with the Gel iX20 imager . Band quantification was performed with Image Studio Lite software .Cyclophilin A was used as housekeeper gene for normalization. The relative expression was determined by the 2−ΔΔCt method using average Ct values [The expression of the circRNA and its corresponding total RNA were determined via RT-qPCR using 2× Power SybrGreen Master Mix . Reactions were run in triplicate on the Applied Biosystems StepOnePlus cycler using the following program: 10 min incubation at 95 °C prior to 40 cycles of 15 s at 95 °C and 30 s at 60 °C. t values . All pri2 in PCR Single Cap 8er-SoftStrips reaction tubes for 60 min at 37 °C in a T100 Thermal cycler  according to manufacturer’s instructions. The reaction was prepared in parallel in duplicates. After incubation, one reaction mix was used for the RT-PCR and the second reaction served as non-enzyme control (no reverse transcriptase was added to this reaction). Protocols were further executed as described in One µg of total RNA was treated with 10 U of RNase R  in 10 µL of total volume with 0.2 µL 50 mM MgClFirst, the minimal Laccase2 intronic sequence, including a multiple cloning site (MCS), from the pcDNA3.1(+) Laccase2 MCS Exon Vector   was amplSecond, the complete sequence of the circRNA was amplified by PCR (see above) followed by sequencing to assess which exons were included. The amplicons were separated on an agarose gel, the target band was purified using the GeneJET Gel extraction kit (ThermoFisher) and the bands were sequenced with SupremeRun Tube Sanger Sequencing .Third, the target sequence was amplified by Q5-PCR (see above), separated on an agarose gel and purified. The circRNA sequence was inserted into the MCS using restriction site cloning with either PacI and AscI or PstI-HF and AscI . Next, the entry clones were recombined into the pEF-DEST51 Gateway expression vector (ThermoFisher) using the LR Clonase II enzyme mix (ThermoFisher) according to manufacturer’s instructions.Lastly and importantly, to delete the remaining sequences of the MCS at both ends of the circRNA target sequences, a two-step PCR protocol was used. With a first PCR, the circRNA target sequence was amplified in two parts using overlapping primer pairs lacking the MCS sequence. In a second step, the outer primers were used to create the full target sequence. This was inserted into the expression vector by restriction enzyme cloning.Cells were seeded in a 6 cm cell culture dish (TPP) and a 12-well plate  and allowed to attach overnight. A 1000–2000/well cells were seeded in the 12-well plate for colony formation assay, 250,000 cells were seeded in the 6 cm dish for RNA isolation. Next day, the cells were transfected with 1 µg (12-well) or 5 µg (6 cm culture dish) of plasmid using 1 mg/mL polyethylenimine (PEI)  in a 1:3 ratio in OPTI-MEM  according to the manufacturer’s instructions. The empty vector was used as negative control.After 48 h, cells from the 6 cm culture dish were lysed and RNA was isolated as previously described. Reverse transcription and quantitative real-time PCR was used to determine the level of overexpression of both the circRNA and the corresponding total (linear) RNA.2O during 10 min at room temperature. Remaining stain was removed by washing with ddH2O and the plate was air-dried overnight. The wells were imaged with a stereo Discovery V8 microscope  equipped with a 0.3× objective and 1.0× zoom and colony areas were determined using ImageJ [For the colony formation assay, cells in the 12-well plate were allowed to grow during 8–17 days, depending on the proliferation rate of the cell line . The medium was changed every 4 days during this period. Afterwards, cells were washed with 1× ice-cold PBS (Sigma) and fixed with 100% methanol  during 10 min at −20 °C. Cells were stained with 0.5% crystal violet (Carl Roth) dissolved in 25% methanol in ddHg ImageJ .g at 4 °C. The lysate was transferred to a new tube. The protein concentration was measured with the BCA assay . Thirty µg of total protein was loaded on a 10% SDS-PAGE gel and run for 1 h at 120V. Next, proteins were semi-dry blotted onto a nitrocellulose membrane (1 h 20 min at 0.07 A). The membrane was blocked in 5% skimmed milk during 1 h at room temperature. The membrane was incubated overnight at 4 °C with the primary antibody for TNFRSF21  in a 1:1000 dilution in 5% BSA in 1× TBS  with 0.1% Tween-20. After washing with 1× TBS with 0.1% Tween-20, the membrane was incubated with the secondary antibody  during 2 h at room temperature. After washing, bands were detected with SuperSignal West Pico Chemiluminescent Substrate (ThermoFisher) and visualized with the Intas ECL Chemocam Imager (Intas). As loading control, membranes were stained for β-Actin  in a 1:10000 dilution in 5% BSA in 1× TBS with 0.1% Tween-20, incubated for 2 h at room temperature. After washing, the membrane was incubated with the secondary antibody for 2 h at room temperature .Cells were seeded in a 6 cm dish and transfected as described in 4.7. After 48 h, cells were lysed on ice in 1× RIPA buffer  and spun down for 15 min at 12,000× T-test after performing an F-test to test scedasticity. Experiments were always performed at least in biological triplicates. The RT-qPCR was performed in three and colony assays were performed in three to six technical replicates per biological replicate. Colony assay replicates with a cell colony number of zero were excluded from further calculations. The correlations per histology or genotype were performed by multiple T-tests (T-test per row) with Bonferroni correction for multiple testing. Groups with a mean value of zero were excluded from statistical analysis.Statistics were performed in Excel  and GraphPad Prism . Statistical significance was assessed with a two-sided CircTNFRSF21 can be translated into a 42 kDa protein crossing the backsplice site in two different reading frames. In conclusion, this lung cell line dataset advances our understanding of circRNA expression and will enable future research on the function and relevance of circRNAs in lung cancer.In this study, we present a comprehensive map of the circRNA landscape in the FL3C panel of 60 lung cell lines based on 2.8 million backsplice reads derived from deep sequencing of rRNA-depleted RNA. Most circRNAs show high cell line specificity and generally a positive correlation between circRNA and total (linear) RNA expression levels. Known cancer genes tend to produce more circRNAs. CircRNA patterns can be used for classification and clustering of cell lines or reflect tumor histology, genotype and proliferation rate. Correlation of cell proliferation with circRNA expression can identify circRNAs whose overexpression has a significant impact on colony formation of lung cancer cells. Moreover,


Single rare cell characterization represents a new scientific front in personalized therapy. Imaging mass cytometry (IMC) may be able to address all these questions by combining the power of MS-CyTOF and microscopy.We have investigated this IMC method using < 100 to up to 1000 cells from human sarcoma tumor cell lines by incorporating bioinformatics-based t-Distributed Stochastic Neighbor Embedding (t-SNE) analysis of highly multiplexed IMC imaging data. We tested this process on osteosarcoma cell lines TC71, OHS as well as osteosarcoma patient-derived xenograft (PDX) cell lines M31, M36, and M60. We also validated our analysis using sarcoma patient-derived CTCs.We successfully identified heterogeneity within individual tumor cell lines, the same PDX cells, and the CTCs from the same patient by detecting multiple protein targets and protein localization. Overall, these data reveal that our t-SNE-based approach can not only identify rare cells within the same cell line or cell population, but also discriminate amongst varied groups to detect similarities and differences.This method helps us make greater inroads towards generating patient-specific CTC fingerprinting that could provide an accurate tumor status from a minimally-invasive liquid biopsy. Circulating tumor cells (CTCs) are rare cells that have been repeatedly demonstrated to contain predictive properties for patient survival –3. The aFine needle aspirates (FNAs) are a commonly used method to extract rare tissue for tumor assessment , 17. ThiTo obtain a several folds higher multiplexed labeling with a similar approach we turned towards the recently developed Imaging mass cytometry (IMC) technology . CytometTo accomplish our goal, we first targeted our approach using human tumor cell lines with a low number (< 100) to assimilate rare cell case and determine feasibility. We also used rare CTCs from patient blood samples as real case study. In the low cell number analysis, we discovered that the colocalization of cell surface vimentin (CSV) positive cells with non CSV expressing cells created a unique protein signature via bioinformatics analysis such as t-Distributed Stochastic Neighbor Embedding (t-SNE) clustering, correlation matrix, paired scatter plot, etc. . These mOHS (RRID:CVCL_B450) and TC71 (RRID:CVCL_2213) cell lines were obtained from NCI depository. Therapy-resistant PDX cell lines M31, M36, and M60 were generously provided by Dr. Richard Gorlick . All celWe use an in-house developed CTC-targeting antibody, 84–1, which is specific for CSV. The procedure for isolating CTCs from whole blood has been previously described [The analyses were performed using R language. Euclidean distance was used in the dendrogram in the cluster heat map. Power analysis was performed using PWR package. t-Distributed Stochastic Neighbor Embedding (t-SNE) was performed using tSNE package with the default parameter setting. The same t-SNE analysis was performed 10 times to confirm the consistency despite the stochastic nature of the method.Fluidigm Hyperion Imaging Mass Cytometer System at the UT MD Anderson flow cytometry & cell imaging core facility was employed for the laser-based cell ablation and imaging (1 μm resolution). Channel-specific signal data is gathered on a per-pixel basis. We used the BitPlane IMARIS software analysis package to mask pixel data into single cell data. These aggregations of pixels were then used to record the signal localization  and intensity per individual cells. Cellular regions were identified by correlating with Ir191/193 which labels DNA, smooth muscle actin (SMA) which labels membrane, and using ImarisCell to mask and identify the region between the nucleus and membrane, which was labeled the cytoplasm. Data shown in heat map is normalized to nuclear labeling signal strength.The antibodies used for this study are listed in Table To be able to more easily detect and discern protein signature in rare cells such as CTCs (out of less than 100 cells) than conventional techniques such as flow cytometry and confocal imaging, we first used sarcoma cell lines TC71 and OHS as platforms for method development. We chose to begin with cell lines since they are easily available and we’ve previously found that even in cell lines, there is inherent heterogeneity in CSV protein expression . We askeWhile our discoveries regarding the sarcoma cell lines are intriguing, they have limited clinical relevance. To further develop towards our overall goal of precise rare cell detection for prognostic relevance, we used < 500 PDX cell line cells developed from therapy-resistant sarcoma patients; we expected these cells to exhibit greater heterogeneity. Whereas in TC71 cells where the CSV- cells immediately surrounding the CSV+ cells were found to be distinct Fig. a, c, in We analyzed CTCs captured from metastatic sarcoma patients’ peripheral blood. We isolated single CTCs as well as CTC clusters, all of which stained positive for CSV and exhibited variable staining of other markers included in the staining panel Fig. a. UnfortWe employed IMC imaging combined with rare cell-tailored bioinformatics analysis to categorize identify outliers among tumor and PDX cell line populations. We used the gathered protein intensity data to identify outlier cells in a small pool of cells (< 500). We envision our approach can be used to profile or identify rare cells within a low cell number pool such as a FNA. This IMC-based protein profiling could help us learn of active signaling pathways and unique signatures that may indicate future tumor behavior. Ideally, profiling rare cells in FNA would help us deteming the degree to which CTCs recapitulate similar protein signature to the rare cells or the dominant population in a tumor. We found that most tumor analysis methods such as needle biopsies are either invasive or bring high risk to patients for such a study or do not fit our needs , 33. ComOur approach can be applied to any rare cell populations that are commonly studied via immunofluorescence methods. Standard techniques rely on either genetic screening or limited-marker based protein profiling . Our metCurrent fluorescent marker technology is limited to 4–6 targets per stain, depending on the detection equipment. While there are strategies to bleach and re-stain, or other means of antibody stripping from the epitope, these methods are not accepted to be completely clean and effective in comparison to fresh staining . FurtherWhile our protocol solves the fast and multiplex analysis of rare cells, it is limited by the constraints of the first generation of IMC machines , 23. TheWe expect that technological improvements over time will address the speed, background noise issues, and localization accuracy of IMC analysis. Next generation IMC hardware will surely increase the co-label limit and increase scan speed, which is not much of a concern even in its current iteration. We anticipate that with wider adoption and interest, the assay-associated costs will also become lower as well.This method helps us make greater inroads towards generating patient-specific CTC fingerprinting that could provide an accurate tumor status from a minimally-invasive liquid biopsy.


Certified cell line models provide ideal experimental platforms to answer countless scientific questions. The LL-100 panel is a cohort of cell lines that are broadly representative of all leukemia–lymphoma entities (including multiple myeloma and related diseases), rigorously authenticated and validated, and comprehensively annotated. The process of the assembly of the LL-100 panel was based on evidence and experience. To expand the genetic characterization across all LL-100 cell lines, we performed whole-exome sequencing and RNA sequencing. Here, we describe the conception of the panel and showcase some exemplary applications with a focus on cancer genomics. Due diligence was paid to exclude cross-contaminated and non-representative cell lines. As the LL-100 cell lines are so well characterized and readily available, the panel will be a valuable resource for identifying cell lines with mutations in cancer genes, providing superior model systems. The data also add to the current knowledge of the molecular pathogenesis of leukemia–lymphoma. Additional efforts to expand the breadth of available high-quality cell lines are clearly warranted. The rationale for using leukemia–lymphoma (LL) cell lines is that they can provide exceptional opportunities to identify valuable information for a deeper understanding of cancer biology complemented by efforts in the development of new therapeutic approaches.With many research projects becoming increasingly molecularly based, genomic characterization of LL cell lines is ever more essential. As sequencing costs have declined significantly, next generation sequencing (NGS) has been applied as a practical strategy for interrogating many genes simultaneously. Indeed, a number of LL cell lines have now been genomically sequenced with data also available on their gene expression profiles ,2,3. TheTaking advantage of this significant technological momentum, we envisaged to close any knowledge deficits in the genomic landscape of a panel of the most valuable LL cell lines, covering the whole spectrum of leukemias and lymphomas . The manner in which this panel evolved from our long-time experience with these cell lines and some exemplary applications as tools for genetic discovery will be the focus of this article.The main reason for the use of LL cell lines is their ability to provide an unlimited source of cellular material that is able to grow indefinitely in vitro  Table 1Table 1. Human leukemia–lymphoma cell lines are characterized by the six following general features : (1) It (5) LL cell lines contain genetic alterations: a survey of the LL cell line entries in a comprehensive compendium showed that among 637 well-characterized cell lines for which karyotypes have been published, only three (0.5%) showed a normal karyotype without any structural or numerical aberrations. However, in two of these latter cell lines extended cytogenetic analysis found both numerical and structural alterations .Besides these gross alterations at the cytogenetic level, all cell lines also carry alterations which are detectable only at the molecular level, e.g., point mutations or deletions in a plethora of genes. These genetic changes presumably provide the affected cell with either proliferative or survival advantages and are thought to play an important role in both the in vivo tumorigenesis and the in vitro establishment of the cell line.(6) Several studies have shown that leukemia–lymphoma cell lines retain gene expression patterns characteristic of tumor cells from patients with similar types of malignancy. An exception may be genes responsible for immortalization which are generally upregulated in tumor cell lines. There have been no longitudinal studies reported hitherto which document significant changes in gene expression from patient to cell line.LL cell lines do have limitations as they were often derived from patients with end-stage disease which carry an extensive and selective mutational load. Thus, the initial cell cultures presumably contain heavily mutated cells, already optimized for in vitro growth . In a liThe ability to grow autonomously in vitro may require transformation by a minimum of several oncogenic hits. This notion could help to explain why the repertoire of LL cell lines does not adequately represent each of the many clinical subentities and molecular subtypes . For exaUnder the conditions of optimal cell culture, LL cell lines generally are stable and preserve the salient features of the original primary cells. Nevertheless, some have objected that the cells may take on certain properties during the attempts to establish a cell line and subsequently during continued culture as so-called in vitro artefacts. However, little evidence has been forwarded to substantiate such arguments. Alternatively, a specific feature of the neoplastic cells may facilitate immortalization or may serve as a prerequisite for immortalization.This question whether certain cell line characteristics are obtained during in vitro growth or already exist in the original material should be addressed experimentally by directly comparing pairs of fresh neoplastic cells (at the single cell level) and the cell lines derived from them. While this issue may be seen as semantic, it nevertheless, is of importance as cell lines are widely used tools in experimental studies of an ever increasing panorama of scientific fields. For example, does the upregulation of critical tumor-associated genes truly reflect the molecular alteration which may cause the disease or its progression, resistance to therapy or other pathophysiological processes? If not, then obviously it would be legitimate to scrutinize the utility of cell lines as bona fide models for such investigations.Finally, deliberate or unintended stress provoked by inappropriate cell culture conditions may lead to selection pressure. Intentional manipulations may result in phenotypic and genotypic shifts.Some have opined that LL cell lines carry an extensive and biased mutational repertoire, further optimized over years or even decades for in vitro growth . Recently, it has been insinuated that selected cell lines  constitute a substantial resource of live experimental and informative models for exploring questions related to mutational processes and their underlying mechanisms . In someCancer cells frequently undergo genomic changes through proliferation, known as clonal evolution, resulting in intra-tumor heterogeneity . This maCancer cell line panels are a useful resource for a myriad of scientific questions. The US National Institute of Cancer 60 human tumor cell line anti-cancer drug screen (NCI-60) was developed in the late 1980s as a disease-oriented in vitro drug discovery tool intended to supplant the use of transplantable tumors in animal models in drug screening ,24. UltiA few years ago, we already attempted to recommend sets of reference LL cell lines as the NCI-60 panel contains only five LL cell lines ,30. ThesFirst, cell lines known to be cross-contaminated, sister cell lines and subclones, controversial cell lines , and cell lines known to be difficult to culture were excluded which left some 520 cell lines in the selection pool. Cell lines which are not available for general distribution were subtracted, thus, 242 cell lines remained. As a final step in the development of this initiative, we build on the data collected over decades in the cell line repository. For the final set we prioritized cell lines on the basis of availability, robust proliferation, and being well-characterized and comprehensively annotated. This disease-oriented concept relies on the fact that the cell lines are considered to be representative for the disease entity. LL cell lines is an umbrella term, encompassing a variety of entities and subentities. The LL entities for which cell lines were selected are based on the two Revised WHO classifications of myeloid and lymphoid neoplasms ,32 Figu. A more detailed breakdown of all disease entities and the assigned cell lines, various prominent cytogenetic and genomic mutations of these cell lines, together with unique distinctive features, is provided in n = 100), completeness of cell line data and availability of other genetic information. It is particulary noteworthy to stress their detailed clinical annotation and their comprehensive profiling . The strengths of the panel include, furthermore, the intensive identity and quality control to which the cell lines have been subjected  and cell cycle dysregulation. The t is also one of the most common translocations in multiple myeloma (MM) and plasma cell leukemia (PCL).FLT3 (FLT3) is a gene that encodes for a tyrosine kinase that is essential in the proliferation and differentiation of hematopoietic cells. FLT3 is the most commonly mutated gene in AML. Mutations in FLT3 most often occur as internal tandem duplication (ITD) and less commonly as point mutations, followed by gene amplification. Another example is the gene FLT3 . Fms-likThese illustrative examples demonstrate that the LL-100 panel can provide insights into the relevance and validity of using cell lines as models for molecular and cellular research. The LL-100 panel offers several unique features, advantages and benefits . All celThis historical perspective is intended to show the conceptual development and implementation of the LL-100 panel and its value and legitimacy as a research tool. Owing to the wide variety of different types of leukemias and lymphomas, the selection of a limited number of cell lines required choices and compromises to be made and entails various limitations.However, our long-term accumulation of pertinent data and our years of experience with the cell lines has permitted the judicious selection of an adequate number of representative cell lines. Hence, we had the unique opportunity to collate in this initiative a panel of 100 cell lines that reflects the diversity of leukemia–lymphoma, stratified into 22 entities.This panel draws its strength from a large size and the quality of the included cell lines. Unlike previous efforts at establishing cell line panels, from the beginning this initiative fully exploited modern karyotyping by the systematic application of classical and molecular cytogenetics, hence, there exists a deep characterization at the genetic level. The unchecked dissemination and reckless use of false LL cell lines is a serious drawback ,48,49,50In order to strengthen the utility of the data for the scientific community, it is critical that the NGS data are freely and publicly available and that also the accompanying cell lines are readily available. These two requirements are here satisfactorily fulfilled.Thus the LL-100 panel cell lines may help to take the understanding of leukemia–lymphoma biology to the next level, expanding the utility of cell lines and increasing their precision as experimental models for many applications . For exaFurther existing and also novel LL cell lines should continue to be integrated in the systematic application of NGS, on one hand adding substantial depth to their obligatory characterization moving beyond traditional features of old and new cell lines, and on the other hand thus expanding the repertoire of informative cell lines. Clearly, to fully sequence a vast repertoire of the most important LL cell lines would be an ambitious, yet feasible, approach to move the field forward. There are valuable online tools that enable access to genomic and phenotypic datasets that were derived from cancer cell lines, including some LL cell lines ,52,53,54In summary, the wealth of genomic data generated from cell lines will hopefully lead to an increase in the number of testable and actionable hypotheses in leukemia–lymphoma pathobiology, as envisaged previously . In someThe key sequencing data have been deposited at the European Nucleotide Archive (ENA) under the accession number PRJEB30297 for WES and PRJEB30312 for RNA-seq, respectively.


FGFR2) gene fusions are bona fide oncogenic drivers in 10–15% of intrahepatic cholangiocarcinoma (CCA), yet currently there are no cell lines publically available to study endogenous FGFR2 gene fusions. The ability of clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 to generate large yet precise chromosomal rearrangements has presented the possibility of engineering endogenous gene fusions for downstream studies. In this technical report, we describe the generation of an endogenous FGFR2–Bicaudal family RNA binding protein 1 (BICC1) fusion in multiple independent cholangiocarcinoma and immortalized liver cell lines using CRISPR. BICC1 is the most common FGFR2 fusion partner in CCA, and the fusion arises as a consequence of a 58-megabase-sized inversion on chromosome 10. We replicated this inversion to generate a fusion product that is identical to that seen in many human CCA. Our results demonstrate the feasibility of generating large megabase-scale inversions that faithfully reproduce human cancer aberrations. Fibroblast growth factor receptor 2 ( FGFR2) gene fusions are present in 10–15% of intra-hepatic CCA [FGFR2 gene, including its kinase domain, is fused to different 3′-fusion partners. The most common partner genes include Periphilin 1 (PPHLN1), Bicaudal family RNA binding protein 1 (BICC1), Adenosylhomocysteinase Like 1 (AHCYL1), and Transforming Acidic Coiled-Coil Containing Protein 3 (TACC3) [FGFR2 gene fusion, only cell lines with ectopic overexpression. Such overexpression has been done in the kinase-dependent Ba/F3 mouse B or the NIH373 mouse embryo fibroblast cell lines that do not necessarily resemble the molecular features of human CCA cells [Cholangiocarcinoma (CCA) is a relatively rare and aggressive cancer arising from epithelial cells of the biliary tree. Fibroblast growth factor receptor 2  . These pFGFR2 fusion-bearing cells, we have used the CRISPR/Cas9 system, which only requires the expression of Cas9 and target-specific sgRNAs [EML-ALK4, CD74-ROS1, KIF5B-RET, FIP1L1-PDGFRA, MLL-ENL, or EWSR1-FLI1 [To create endogenous c sgRNAs  to inducSR1-FLI1 ,9,10,11.FGFR2–BICC1 fusion is the result of an inversion of a 58-megabase fragment on chromosome 10  and primers designed to flank the genomic breakpoint junctions, 6 of the sgRNA combinations were found to efficiently introduce a genomic inversion resulting in an FGFR2–BICC1 fusion . The sgRNA combination sgFGFR2-2/sgBICC1-1  with in vitro . FGFR2–BICC1 fusion and the other end of the inversion, the BICC1–FGFR2 fusion, were detected as expected , both the expected a. Additid breaks b, withouFGFR2–BICC1 fusion is expressed in our clonal cell lines, and were able to detect the transcript by RT-PCR  was cloned into another other  using the PciI restriction site upstream of that cassette. The resulting multiplex plasmid thus contains both sgRNAs under the control of two independent U6 promoters.CRISPR/Cas9 target sites were selected using Benchling gRNA design tool and were cloned into the pX330 plasmid expressing the gRNA under the control of an U6 promoter, a Cas9 expression cassette, and mCherry as a selection marker. To obtain the FGFR2–BICC1 fusion we designed four sgRNAs located in the intron between exon 17 and 18 of the FGFR2 gene, three sgRNAs located in the intron between exon 2 and exon 3 of The immortalized hepatocyte cell line HC3716-hTERT was used for all experiments. Cells were transiently transfected with the pX330 plasmids using lipofectamine 3000 as described by the manufacturer. To enrich for transfected cells, mCherry positive cells were obtained using fluorescence-activated cell sorting (FACS) 48 h after transfection.FGFR2–BICC1 fusion. Cells were seeded in a 96-well plate with a concentration of 10 cells per ml, resulting in approximately 1 cell per well. Cells were expanded for 2–3 weeks, and wells containing multiple colonies and hence polyclonal cell lines were excluded.Limiting dilution was used to obtain clonal cell lines with the 4)2SO4. Primers located on the FGFR2 gene and the BICC1 gene were used in different combinations to detect either WT FGFR2, WT BICC1, the FGFR2–BICC1 fusion, the BICC1–FGFR2 fusion, a deletion event between the two target sites, or a duplication. Primer combinations and primer sequences are listed in FGFR2–BICC1 fusion, the PCR products of clonal cell lines were analyzed by Sanger sequencing. Either mixed cell populations obtained before limiting dilution or clonal cell lines were analyzed for the presence of genomic rearrangements by using PCR. DNA was isolated using a DNA isolation buffer containing Proteinase K, Tween, Triton-X, KCl, and . Purified PCR product was annealed in a thermocycler and subsequently incubated with T7 Endonuclease I. Reactions were analyzed on a gel, and the editing efficiency was calculated as described by Ran et al. .FGFR2–BICC1 transcript, Trizol was used to isolate total RNA. QIAGEN OneStep RT-PCR kit was used to obtain cDNA, which was used in a subsequent PCR to detect the expression of the fusion transcript. The forward and reverse primers are located on exon 17 FGFR2 and on the exon 3 of BICC1, respectively, meaning that the PCR product spans the exon–exon junction of the fusion transcript. GAPDH was used as a control. Primer sequences are listed in To analyze the expression of the FGFR2–BICC1 fusion at a rate of 1–15% across four cell lines. We will make the dual-sgRNA plasmid available on Addgene for public use. We suggest that other gene fusions with FGFR2 can also be readily created through adaptions of our CRISPR/Cas9 approach, as many of the fusion partners also reside on chromosome 10. It is clear that CRISPR technology is a powerful method for reproducing a wide range of human genetic aberrations, and our results demonstrate that even very large chromosome rearrangement events can be efficiently generated using a single multiplex plasmid.Overall, our approach generated the


Evidences indicated that non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) might originate from the same cell type, which however ended up to be two different subtypes of lung carcinoma, requiring different therapeutic regimens. We aimed to identify the differences between these two subtypes of lung cancer by using integrated proteome and genome approaches.Two representative cell lines for each lung cancer subtype were comparatively analysed by quantitative proteomics, and their corresponding transcriptomics data were obtained from the Gene Expression Omnibus database. The integrated analyses of proteogenomic data were performed to determine key differentially expressed proteins that were positively correlated between proteomic and transcriptomic data.The proteomics analysis revealed 147 differentially expressed proteins between SCLC and NSCLC from a total of 3,970 identified proteins. Combined with available transcriptomics data, we further confirmed 14 differentially expressed proteins including six known and eight new lung cancer related proteins that were positively correlated with their transcriptomics data. These proteins are mainly involved in cell migration, proliferation, and invasion.PXD015270.The proteogenomic data on both NSCLC and SCLC cell lines presented in this manuscript is complementary to existing genomic and proteomic data related to lung cancers and will be crucial for a systems biology-level understanding of the molecular mechanism of lung cancers. The raw mass spectrometry data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier  Lung cancer is the leading cause of cancer death in the world. According to the cancer statistics in 2018, the number of new cases and deaths reached the top  in the world. The incidence and mortality of lung cancer presented for 11.6% and 18.4% in the world . World HConsidering the huge differences between NSCLC and SCLC in their diagnosis and therapeutic regimens, it is important for us to understand the essential differences between these two subtypes of lung cancers. Comparative analysis of NSCLC and SCLC have been performed using different high-throughput approaches, such as genomics, transcriptomics and proteomics. A genome-wide allelotyping study in 2001 showed that tumor suppressor genes were significantly different between NSCLC and SCLC. In an integrated COSMIC database created by 2016 , TP53, RHere, we comparatively analysed the protein expressions of the NSCLC cell lines (A549 and H1975) and the SCLC cell lines (H446 and H69) using quantitative proteomics to identify differentially expressed proteins (DEPs) between these two subtypes of lung cancers. In addition, their transcriptomic data were also downloaded from Gene Expression Omnibus (GEO) public database. Various bioinformatic approaches, such as Gene Ontology enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein–protein interaction (PPI) network integration were applied for the investigation of the DEPs (and genes). With the comparison of proteomics and genomics data, we pinpointed several gene/protein candidates with potential biological significances, of which the possible functions in lung cancer were further discussed. To our knowledge, this is the first study to investigate differences between NSCLC and SCLC by integrated proteomics and transcriptomics analyses. Our data may provide potential biological candidates for further study of lung cancer as well as to understand the molecular differences between NSCLC and SCLC.2. The NSCLC cell lines were cultivated with adherent method and SCLC cell lines were cultivated in suspension method. After removing the medium, the cells were washed with phosphate buffered saline  buffer and then lysed directly with 8 M urea/1 M NH4HCO3 solution  and two SCLC cell lines  were obtained from American Type Culture Collection (ATCC). The cell lines were cultivated in the RPMI-1640 culture medium supplemented with 10% fetal bovine serum (FBS) and 1% Penicillin-Streptomycin Solution in a humidified incubator at 37 °C and 5% COsolution . Lysates4HCO3 buffer, sonicated by Ultrasonic Cell Distribution System, reduced by 5 mM DTT at 37 °C for 1 h and alkylated by 15 mM IAM at room temperature in the dark for 30 min. The solutions were diluted two-fold with deionized water, and the proteins were digested with sequencing grade trypsin  at 37 °C for 2 h with shaking. The solutions were further diluted by four-fold, and additional trypsin  were added and incubated at 37 °C overnight with shaking. Then samples were centrifuged at 15,000 g for 10 min to remove cell residues and desalted with HLB column . The peptides were eluted by 60% ACN/0.1% TFA.The protein extraction from cell lines and protein digestion were performed as described previously with minor modifications . Briefly5) were collected from 350–1,800 m/z at a resolution of 60 K followed by data dependent HCD MS/MS  using an isolation width of 1.6 Da. Charge state screening was enabled to reject unassigned and singly charged ions. A dynamic exclusion time of 45 s was used to discriminate against previously selected ions.Each sample underwent triplicate LC-MS/MS runs on an Orbitrap Fusion Lumos mass spectrometer . Peptides were separated on a nano Easy-LC system with a 75 µm ×  15 cm Acclaim PepMap100 separating column protected by a two cm guard column . The mobile phase flow rate was 300 nL/min and consisted of 0.1% formic acid in water (A) and 0.1% formic acid 80% acetonitrile (B). The gradient profile was set as follows: 3–7% B for 2 min, 7–35% B for 83 min, 35–68% B for 20 min, 68–100% B for 5 min and equilibrated in 100% B for 10 min. MS analysis was performed using a mass spectrometer. The spray voltage was set at 2.3 kV. Orbitrap MS1 spectra  using MaxQuant (version 1.6.3.3)  . The pre1.6.3.3) . The “Ma1.6.3.3) . For the1.6.3.3) . The medGSE36133 which was obtained from GEO database (http://www.ncbi.nlm.nih.gov/geo/)   were dowov/geo/) , and basov/geo/) . For datov/geo/) , and theov/geo/) . The pro2 FC—) ≥1 and P-value <0.01. The screened DEGs underwent two-way hierarchical clustering analysis by using the pheatmap package in R language.Linear Models for Microarray Analysis package in R language was employed to screen DEGs between NSCLC samples and SCLC samples. The strict thresholds were set at fold-change (—loghttps://david.ncifcrf.gov/) followed by the ggplot2 R language package  analysis was undertaken for the significantly different expressed genes and proteins in order to find the unique biological process, cellular component and molecular function. GO enrichment analysis was performed by DAVID ( package . Kyoto E package  to displ package . KEGG pahttps://string-db.org/) was used to query whether the proteins interacted with other proteins with combined score output (Protein interaction was constructed using the significantly different expressed genes and proteins. The STRING website (e output . In thishttps://www.reactome.org/) with the significance threshold of P-value<0.05, FDR<0.01 . Most of the DEPs were enriched in the citrate cycle (TCA cycle), pyruvate metabolism, valine, leucine and isoleucine degradation, biosynthesis of unsaturated fatty acids, protein processing in endoplasmic reticulum, oxidative phosphorylation, mRNA surveillance pathway, and pentose phosphate pathway  network was performed by Cytoscape to investigate the biological and physiological connections among DEPs . CompareTo better demonstrate the interconnections of proteins in the interaction network, proteins involved in RNA processing were displayed , which iTo further determine the key proteins between NSCLC and SCLC cells, we compared the proteomic data with published microarray-based transcriptomic data from the same cell lines  Fig. 5AFig. 5A. Reactome enrichment indicated that nine of these 14 proteins were enriched in membrane-bounded vesicle, mast cell granule, compact myelin, Schmidt-Lanterman incisures, extracellular exosome and myelin sheath . The bioLung cancer is the leading cause of cancer death in the world. Due to the different pathway regulations, NSCLC and SCLC required different therapy regimens. The aim of this study was to investigate the DEPs between NSCLC and SCLC, which could be helpful in understanding the development of the disease and the search of possible treatment targets. The quantitative proteomics approach was used to identify the proteins differentially expressed between NSCLC and SCLC by analysing two representing cell lines per subtype. The GEO data were then used as a supplement for the data analysis and determination of pivotal proteins. Among 3,970 proteins identified from four cell lines, a total of 147 were determined to be differentially expressed between two lung cancer subtypes. The majority of proteins showed no difference between NSCLC and SCLC, implying the same or similar origin of these two lung cancer types .The results of GO and KEGG pathway analyses showed that these DEPs were enriched in several different pathways, biological processes, cellular components and molecular functions. Based on the GO analysis, the nucleus, DNA binding, as well as DNA repair may be involved in cell replication, and protein binding, membrane might be involved in cell recognition. Therefore, these up-regulated proteins in SCLC might be associated with cell proliferation and recognition. Additionally, the down-regulated proteins might be associated with cell adhesion and migration such as cell–cell junction, movement of cell or subcellular component.According to the results from the PPI network, we found some highly interconnected proteins. Among these proteins, the results of enrichment revealed that mRNA processing was closely associated with cancer initiation and development, which were consistent with previous studies . NumerouThe majority of these proteins have been proved to be associated with cancers. Among these nine proteins, ANXA2 is a membrane-bound protein that is usually relevant to cell invasion and metastasis . It was Based on a report published in 2015, knockdown of FLNB in A549 cell line resulted in slow down of invasion ability compared with normal A549 cell line. FLNB enhanced invasion of lung cancer cells through phosphorylation of MRLC and FAK . TherefoIn addition, ME2 also played a crucial role in lung cancer growth. ME2 encodes a mitochondrial NAD-dependent malic enzyme which was highly expressed in lung cancer . In A549In addition, the other eight proteins have also been reported in other types of cancers and may be closely associated with lung cancer proliferation or invasion. For instance, it has been validated high expression of Anp32E are associated with shorter survival time and high risk of disease relapse in Triple- negative breast cancer . AKR1B1 In summary, with a combined use of quantitative proteomics analysis and their corresponding transcriptome data, we identified 14 DEPs among NSCLC and SCLC cell lines. Bioinformatics analysis indicated that these proteins are mainly involved in the cell migration, proliferation and invasion, and many of them has been reported to be associated with cancers. Due to the limited number of cell lines used, the results presented in this manuscript might still require further validations. Even through, this research still revealed important proteogenomic differences between NSCLC and SLCL cells, which can be complementary to existing genomic and proteomic data related to lung cancers and will be crucial for a systems biology-level understanding of the molecular mechanism of lung cancers.10.7717/peerj.8779/supp-1Supplemental Information 1(S1) All proteins identified from four different NSCLC or SCLC cell lines. (S2) Quantitative proteins among four different cell lines with at least five PSMs. (S3) Differentially expressed proteins in small-cell lung cancer (SCLC) cell lines comparing with non-small-cell lung cancer cell lines (NSCLC). (S4) Fourteen differetially expressed proteins that positively correlated with their mRNA.Click here for additional data file.