Label-free tumor cells classification using deep learning and high-content imaging.

Many studies have shown that cellular morphology can be used to distinguish spiked-in tumor cells in blood sample background. However, most validation experiments included only homogeneous cell lines and inadequately captured the broad morphological heterogeneity of cancer cells. Furthermore, normal, non-blood cells could be erroneously classified as cancer because their morphology differ from blood cells. Here, we constructed a dataset of microscopic images of organoid-derived cancer and normal cell with diverse morphology and developed a proof-of-concept deep learning model that can distinguish cancer cells from normal cells within an unlabeled microscopy image. In total, more than 75,000 organoid-drived cells from 3 cholangiocarcinoma patients were collected. The model achieved an area under the receiver operating characteristics curve (AUROC) of 0.78 and can generalize to cell images from an unseen patient. These resources serve as a foundation for an automated, robust platform for circulating tumor cell detection.

Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images.

In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived from cell phase images. Validation of the developed classifier shows the accuracy for distinguishing between the three cell types of about 93% and between different cell states of the same cell line of about 89%. In the field test of the developed algorithm, we demonstrate successful evaluation of the temporal dynamics of relative amounts of live, apoptotic and necrotic cells after photodynamic treatment at different doses.

Characterization of a new case of XMLV (Bxv1) contamination in the human cell line Hep2 (clone 2B).

The use of misidentified cell lines contaminated by other cell lines and/or microorganisms has generated much confusion in the scientific literature. Detailed characterization of such contaminations is therefore crucial to avoid misinterpretation and ensure robustness and reproducibility of research. Here we use DNA-seq data produced in our lab to first confirm that the Hep2 (clone 2B) cell line (Sigma-Aldrich catalog number: 85011412-1VL) is indistinguishable from the HeLa cell line by mapping integrations of the human papillomavirus 18 (HPV18) at their expected loci on chromosome 8. We then show that the cell line is also contaminated by a xenotropic murine leukemia virus (XMLV) that is nearly identical to the mouse Bxv1 provirus and we characterize one Bxv1 provirus, located in the second intron of the pseudouridylate synthase 1 (PUS1) gene. Using an RNA-seq dataset, we confirm the high expression of the E6 and E7 HPV18 oncogenes, show that the entire Bxv1 genome is moderately expressed, and retrieve a Bxv1 splicing event favouring expression of the env gene. Hep2 (clone 2B) is the fourth human cell line so far known to be contaminated by the Bxv1 XMLV. This contamination has to be taken into account when using the cell line in future experiments.

CELLector: Genomics-Guided Selection of Cancer In Vitro Models.

Selecting appropriate cancer models is a key prerequisite for maximizing translational potential and clinical relevance of in vitro oncology studies. We developed CELLector: an R package and R Shiny application allowing researchers to select the most relevant cancer cell lines in a patient-genomic-guided fashion. CELLector leverages tumor genomics to identify recurrent subtypes with associated genomic signatures. It then evaluates these signatures in cancer cell lines to prioritize their selection. This enables users to choose appropriate in vitro models for inclusion or exclusion in retrospective analyses and future studies. Moreover, this allows bridging outcomes from cancer cell line screens to precisely defined sub-cohorts of primary tumors. Here, we demonstrate the usefulness and applicability of CELLector, showing how it can aid prioritization of in vitro models for future development and unveil patient-derived multivariate prognostic and therapeutic markers. CELLector is freely available at https://ot-cellector.shinyapps.io/CELLector_App/ (code at https://github.com/francescojm/CELLector and https://github.com/francescojm/CELLector_App).

Evaluation of colorectal cancer subtypes and cell lines using deep learning.

Colorectal cancer (CRC) is a common cancer with a high mortality rate and a rising incidence rate in the developed world. Molecular profiling techniques have been used to better understand the variability between tumors and disease models such as cell lines. To maximize the translatability and clinical relevance of in vitro studies, the selection of optimal cancer models is imperative. We have developed a deep learning-based method to measure the similarity between CRC tumors and disease models such as cancer cell lines. Our method efficiently leverages multiomics data sets containing copy number alterations, gene expression, and point mutations and learns latent factors that describe data in lower dimensions. These latent factors represent the patterns that are clinically relevant and explain the variability of molecular profiles across tumors and cell lines. Using these, we propose refined CRC subtypes and provide best-matching cell lines to different subtypes. These findings are relevant to patient stratification and selection of cell lines for early-stage drug discovery pipelines, biomarker discovery, and target identification.

Comprehensive Profiling of Surface Gangliosides Extracted from Various Cell Lines by LC-MS/MS.

Gangliosides act as a surface marker at the outer cellular membrane and play key roles in cancer cell invasion and metastasis. Despite the biological importance of gangliosides, they have been still poorly characterized due to the lack of effective analytical tools. Herein, we performed molecular profiling and structural elucidation of intact gangliosides in various cell lines including CFPAC1, A549, NCI-H358, MCF7, and Caski. We identified and quantified a total of 76 gangliosides on cell membrane using C18 LC-MS/MS. Gangliosides found in each cell line exhibited high complexity and diversity both qualitatively and quantitatively. The most abundant species was GM3(d34:1) in CFPAC1, NCI-H358, and MCF7, while GM2(d34:1) and GM1(d34:1) were major components in A549 and Caski, respectively. Notably, glycan moieties showed more diversity between cancer cell lines than ceramide moieties. In addition, noncancerous pancreatic cell line (hTERT/HPNE) could be distinguished by gangliosides containing different levels of sialic acid compared with cancerous pancreatic cell line (CFPAC1). These results clearly demonstrated the feasibility of our analytical platform to comprehensive profile of cell surface gangliosides for identifying cell types and subgrouping cancer cell types.

Nonlinear mixed-effects models for modeling in vitro drug response data to determine problematic cancer cell lines.

Cancer cell lines (CCLs) have been widely used to study of cancer. Recent studies have called into question the reliability of data collected on CCLs. Hence, we set out to determine CCLs that tend to be overly sensitive or resistant to a majority of drugs utilizing a nonlinear mixed-effects (NLME) modeling framework. Using drug response data collected in the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC), we determined the optimal functional form for each drug. Then, a NLME model was fit to the drug response data, with the estimated random effects used to determine sensitive or resistant CCLs. Out of the roughly 500 CCLs studies from the CCLE, we found 17 cell lines to be overly sensitive or resistant to the studied drugs. In the GDSC, we found 15 out of the 990 CCLs to be excessively sensitive or resistant. These results can inform researchers in the selection of CCLs to include in drug studies. Additionally, this study illustrates the need for assessing the dose-response functional form and the use of NLME models to achieve more stable estimates of drug response parameters.

Proteomic Comparison of Malignant Human Germ Cell Tumor Cell Lines.

Malignant germ cell tumors (GCT) are the most common malignant tumors in young men between 18 and 40 years. The correct identification of histological subtypes, in difficult cases supported by immunohistochemistry, is essential for therapeutic management. Furthermore, biomarkers may help to understand pathophysiological processes in these tumor types. Two GCT cell lines, TCam-2 with seminoma-like characteristics, and NTERA-2, an embryonal carcinoma-like cell line, were compared by a quantitative proteomic approach using high-resolution mass spectrometry (MS) in combination with stable isotope labelling by amino acid in cell culture (SILAC). We were able to identify 4856 proteins and quantify the expression of 3936. 347 were significantly differentially expressed between the two cell lines. For further validation, CD81, CBX-3, PHF6, and ENSA were analyzed by western blot analysis. The results confirmed the MS results. Immunohistochemical analysis on 59 formalin-fixed and paraffin-embedded (FFPE) normal and GCT tissue samples (normal testis, GCNIS, seminomas, and embryonal carcinomas) of these proteins demonstrated the ability to distinguish different GCT subtypes, especially seminomas and embryonal carcinomas. In addition, siRNA-mediated knockdown of these proteins resulted in an antiproliferative effect in TCam-2, NTERA-2, and an additional embryonal carcinoma-like cell line, NCCIT. In summary, this study represents a proteomic resource for the discrimination of malignant germ cell tumor subtypes and the observed antiproliferative effect after knockdown of selected proteins paves the way for the identification of new potential drug targets.

Genetic Ancestry Analysis Reveals Misclassification of Commonly Used Cancer Cell Lines.

BACKGROUND: Given the scarcity of cell lines from underrepresented populations, it is imperative that genetic ancestry for these cell lines is characterized. Consequences of cell line mischaracterization include squandered resources and publication retractions. METHODS: We calculated genetic ancestry proportions for 15 cell lines to assess the accuracy of previous race/ethnicity classification and determine previously unknown estimates. DNA was extracted from cell lines and genotyped for ancestry informative markers representing West African (WA), Native American (NA), and European (EUR) ancestry. RESULTS: Of the cell lines tested, all previously classified as White/Caucasian were accurately described with mean EUR ancestry proportions of 97%. Cell lines previously classified as Black/African American were not always accurately described. For instance, the 22Rv1 prostate cancer cell line was recently found to carry mixed genetic ancestry using a much smaller panel of markers. However, our more comprehensive analysis determined the 22Rv1 cell line carries 99% EUR ancestry. Most notably, the E006AA-hT prostate cancer cell line, classified as African American, was found to carry 92% EUR ancestry. We also determined the MDA-MB-468 breast cancer cell line carries 23% NA ancestry, suggesting possible Afro-Hispanic/Latina ancestry. CONCLUSIONS: Our results suggest predominantly EUR ancestry for the White/Caucasian-designated cell lines, yet high variance in ancestry for the Black/African American-designated cell lines. In addition, we revealed an extreme misclassification of the E006AA-hT cell line. IMPACT: Genetic ancestry estimates offer more sophisticated characterization leading to better contextualization of findings. Ancestry estimates should be provided for all cell lines to avoid erroneous conclusions in disparities literature.

Evaluation genotypes of cancer cell lines HCC1954 and SiHa by short tandem repeat (STR) analysis and DNA sequencing.

Cancer cell lines are used worldwide in biomedical researches, and data interpretation solely depends on unambiguous attribution of those respective cell lines to its original sources. Approximately one-third of all cell lines have an origin other than that assumed, leading to invalid results. It is necessary to characterize the origin of cell lines. Short-tandem-repeat (STR) fingerprinting (DNA fingerprinting) is the method for characterization of genetic identity in cultured cell lines under certain experimental conditions. We showed the fingerprinting profiles in a summed and unidentified human cancer cell line comparison to HCC1954 cell line, revealing marked alterations in DNA fingerprinting profiles up to fourteen STR loci from 16 loci. Furthermore, Sanger DNA sequencing showed no c.3140A > G heterozygous mutation in the PIK3CA gene of this suspected HCC1954 cell line. In addition, we showed the fingerprinting profiles in an unidentified cancer cell line comparison to SiHa cervical cell line, revealing same DNA fingerprinting profiles. In conclusion, we have successfully authenticated and identified both suspected HCC1954 and SiHa cell lines by STR analysis and DNA sequencing. STR analysis combined DNA sequencing may be very useful to evaluate genotypes of cancer cell lines in our cancer studies, as well as in judicial authentication and forensic sciences.

Local-dependency of morphological and optical properties between breast cancer cell lines.

Breast cancer is the most malignant type of cancer in women and is a global health problem, with mortality by metastasis being the main factor among others. Currently, detection and diagnosis of breast cancer is achieved through a variety of procedures, such as clinical examination, medical imaging, biopsy, and histopathological analysis. In contrast, spectroscopic analysis has a variety of advantages such as being noninvasive, not destroying biological materials, and not requiring additional histological analysis. In this study, various approaches using Raman spectroscopy, atomic force microscopy (AFM), and optical microscopy were used together to differentiate between and characterize normal breast cell lines (MCF-10A) and breast cancer cell lines (MDA-MB-231, MDA-MB-453). Raman spectra of normal breast cell and breast cancer cell lines confirmed visual differences in the concentrations of various compounds. These spectra were also analyzed using principle component analysis (PCA), and the PCA results showed reliable separation of the three cell lines and the cancer cell lines (MDA-MB-231, MDA-MB-453). With these results, optically synchronizing the AFM morphology, the Raman spectroscopy, and the visible RGB optical transmission intensity provided contrasts for not only conformational differences but also intracellular variation between the normal and cancer cell lines. We observed the inherent characteristic that there is no local difference in cancer cells regardless of morphology in a wide range of optical properties such as absorption, scattering and inelastic scattering.

Desenvolvimento de xenotransplantes de tumores pancreáticos humanos para varredura genética de alvos moleculares com potencial terapêutico / Establishment of xenografts from human pancreatic tumors for genetic screening of molecular targets with therapeutic potential

O adenocarcinoma ductal pancreático (PDAC, pancreatic ductal adenocarcinoma), o tipo mais prevalente de câncer do pâncreas, é uma neoplasia extremamente agressiva e com elevado índice de letalidade. Há uma necessidade premente de identificação de vulnerabilidades no PDAC que possam ser exploradas como alvos terapêuticos, e a utilização de modelos pré-clínicos que recapitulem a complexidade biológica e heterogeneidade clínica da doença é um aspecto central para a realização dessa tarefa. Os xenotransplantes de tecido tumoral derivado de pacientes (PDX, patient-derived tumor tissue xenografts), realizados em camundongos imunodeficientes, replicam com grande similaridade as principais características do tumor original e, assim, constituem uma ferramenta valiosa para o teste de drogas e estudos funcionais. Neste trabalho, 17 amostras cirúrgicas de PDAC humano foram implantadas subcutaneamente em camundongos nude atímicos. Sete tumores (41%) foram enxertados com sucesso e têm sido mantidos em sucessivas gerações de animais receptores. O exame histológico de seis desses xenoenxertos identificou características morfológicas compatíveis com os padrões reconhecidos no PDAC humano, assim como uma consistente similaridade de seu status de diferenciação histológica em relação aos perfis verificados nos tumoresoriginais. O cultivo in vitro de células derivadas de um dos xenotumores resultou em uma nova linhagem de câncer de pâncreas, com morfologia e cinética de crescimento comparáveis às de outras linhagens celulares de câncer pancreático. O potencial tumorigênico dessa nova linhagem foi validado in vivo, com uma consistente formação de tumores após inoculação em camundongos nude. A fim de aproveitar esse recurso para a investigação de potenciais alvos terapêuticos no PDAC, um rastreamento de vulnerabilidades moleculares foi realizado por meio de silenciamento gênico em larga-escala com RNA de interferência (RNAi). Uma biblioteca lentiviral de 4492 shRNAs (short hairpin RNAs), alvejando cerca de 350 genes envolvidos na regulação epigenética, foi empregada para a triagem de genes de suscetibilidade nas células derivadas de PDX, e em outras cinco linhagens tumorais pancreáticas (AsPC-1, BxPC-3, Capan-1, MIA PaCa-2 e PANC-1). Inicialmente, foi realizada uma série de experimentos preliminares, visando à amplificação e controle de qualidade da biblioteca de silenciamento, à produção de vetores lentivirais e à padronização das condições experimentais para a transdução e seleção das células-alvo. Apenas três das linhagens avaliadas (AsPC-1, MIA PaCa-2 e PANC-1) mostraram-se permissíveis à transdução pelos vetores lentivirais, e foram assim utilizadas no screening de alvos epigenéticos. A análise dos dados obtidos nesse ensaio está em curso e os resultados serão utilizados para a definição de potenciais alvos candidatos. Em conclusão, recursos valiosos para apoiar a pesquisa sobre o câncer de pâncreas foram desenvolvidos. A coleção de PDXs estabelecida, bem como a linhagem celular recém-derivada, constituem uma fonte permanente e estável de células de PDAC para análises moleculares e estudos funcionais que busquem elucidar aspectos da doença ainda pouco compreendidos. Adicionalmente, os reagentes gerados e a expertise adquirida com os ensaiosrealizados com a biblioteca de shRNAs contra alvos epigenéticos serão de grande utilidade em futuras investigações para identificar genes com funções importantes na manutenção do fenótipo tumoral, e consequentemente com potencial para serem explorados terapeuticamente

Pancreatic ductal adenocarcinoma (PDAC), the most prevalent type of pancreatic cancer, is a highly aggressive and lethal neoplasm. There is a pressing need to identify vulnerabilities in PDAC suited to be exploited as therapeutic targets, and the use of preclinical models recapitulating the biological complexity and clinical heterogeneity of the disease is central to this task. Patient-derived tumor tissue xenografts (PDX), established in immunodeficient mice, replicate with great similarity the main characteristics of the original tumor and thus constitute a valuable tool for drug testing and functional studies. In this work, 17 surgical samples of human PDAC were implanted subcutaneously in athymic nude mice. Seven tumors (41%) were successfully grafted and have been maintained through successive generations of recipient animals. Histological examination of six of these xenografts identified morphological characteristics compatible with the recognized patterns of human PDAC, as well as a consistent similarity of their histological differentiation status in relation to the profiles verified in the original tumors. In vitro culture of cells derived from one of these xenografts resulted in a new pancreatic cancer cell line, with morphology and growth kinetics comparable to those of other pancreatic tumor cells. The tumorigenic potential of this freshly derived cell line was validated in vivo, with a consistent tumor formation following inoculation into nude mice. To take advantage ofthis resource to investigate potential therapeutic targets in PDAC, a screening of molecular vulnerabilities was performed through large-scale gene silencing with RNA interference (RNAi). A lentiviral library containing 4492 short hairpin RNAs (shRNAs), targeting about 350 genes involved in epigenetic regulation, was employed for the search of susceptibility genes in the PDX-derived cells and in other five pancreatic tumor cell lines (AsPC-1, BxPC -3, Capan-1, MIA PaCa-2 and PANC-1). Initially, a series of preliminary experiments were carried out aiming at the amplification and quality control of the silencing library, production of lentiviral vectors and adjustment of the experimental conditions for transduction and selection of the target cells. Only three of the cell lines evaluated (AsPC-1, MIA PaCa-2 and PANC-1) were permissible for transduction by the lentiviral vectors, and were accordingly used in the screening of epigenetic targets. The analysis of data obtained in this trial is ongoing and the results will be used for definition of potential candidate targets. In conclusion, valuable resources to support research on pancreatic cancer have been developed. The established collection of PDXs as well as the newly derived cell line constitutes a permanent and stable source of PDAC cells for molecular analyzes and functional studies seeking to elucidate aspects of this disease that are still poorly understood. Additionally, both the reagents generated and the expertise gained from the RNAi assay against epigenetic targets will have inordinate usefulness in future investigations to identify genes with major functions in maintaining the malignant phenotype, and consequently with the potential to be exploited therapeutically

Derivation and basic characterization of colorectal carcinoma primary cell lines.

BACKGROUND: Colorectal carcinoma is one of the most common malignancies in western countries. Among different approaches to its research, primary cancer cell lines can play an important role. AIM: The main purposes of this study were: 1) to establish an effective and reproducible method of colorectal cancer cell isolation and cultivation from primary tumours and lymph node metastases and 2) to elucidate the biological features of the tumours favouring successful cell cultivation. MATERIALS AND METHODS: The tumour cells were obtained from colectomy specimens. Primary tumour and lymph node metastasis tissue was used for establishing the tissue cultures. Colectomy samples were further processed for routine histopathological assessment: tumour grade, stage, angioinvasion and perineural spread were evaluated. Features of tissue culture cells were assessed using phase contrast microscopy and immune-histochemical techniques. WST-1 assay and X-CELLigence real time analysis were carried out for viability and proliferation testing before and after treatment with irinotecan and oxaliplatin. Molecular features of the tumour including K-RAS/B-RAF/N-RAS mutations were tested using allele-specific PCR. Results of the cultivation process were compared to the histopathological and molecular features of the tumours. RESULTS: In total, we obtained 33 samples from the primary site of tumours and 20 samples from lymph node metastases; in total, 27 cell lines were successfully isolated. Morphologic features characteristic of tumour cells in primary cell lines and epithelial differentiation (positive for AE1/AE3 cytokeratin) were evaluated. Higher tumour stage, angioinvasion and presence of perineural spread in primary tumour correlated positively with successful cell isolation from lymph node metastasis. All samples tested were NRAS wild-type. No correlation was found between molecular phenotype and the cell culture features. A higher proliferation potential was observed in the primary tumour cells, whereas higher sensitivity to irinotecan was found in the lymph node metastatic cells. CONCLUSIONS: Using mechanical dissociation, we successfully derived and cultivated CRC cells from primary tumours and lymph node metastases with success rate 3 % and 70% respectively. Primary tumour features favouring successful establishment of cell cultures were identified.

Classification of cancer cell lines using matrix-assisted laser desorption/ionization time­of­flight mass spectrometry and statistical analysis.

Over the past decade, matrix-assisted laser desorption/ionization time­of­flight mass spectrometry (MALDI­TOF MS) has been established as a valuable platform for microbial identification, and it is also frequently applied in biology and clinical studies to identify new markers expressed in pathological conditions. The aim of the present study was to assess the potential of using this approach for the classification of cancer cell lines as a quantifiable method for the proteomic profiling of cellular organelles. Intact protein extracts isolated from different tumor cell lines (human and murine) were analyzed using MALDI­TOF MS and the obtained mass lists were processed using principle component analysis (PCA) within Bruker Biotyper® software. Furthermore, reference spectra were created for each cell line and were used for classification. Based on the intact protein profiles, we were able to differentiate and classify six cancer cell lines: two murine melanoma (B16­F0 and B164A5), one human melanoma (A375), two human breast carcinoma (MCF7 and MDA­MB­231) and one human liver carcinoma (HepG2). The cell lines were classified according to cancer type and the species they originated from, as well as by their metastatic potential, offering the possibility to differentiate non­invasive from invasive cells. The obtained results pave the way for developing a broad­based strategy for the identification and classification of cancer cells.

Heterogeneity of neuroblastoma cell identity defined by transcriptional circuitries.

Neuroblastoma is a tumor of the peripheral sympathetic nervous system, derived from multipotent neural crest cells (NCCs). To define core regulatory circuitries (CRCs) controlling the gene expression program of neuroblastoma, we established and analyzed the neuroblastoma super-enhancer landscape. We discovered three types of identity in neuroblastoma cell lines: a sympathetic noradrenergic identity, defined by a CRC module including the PHOX2B, HAND2 and GATA3 transcription factors (TFs); an NCC-like identity, driven by a CRC module containing AP-1 TFs; and a mixed type, further deconvoluted at the single-cell level. Treatment of the mixed type with chemotherapeutic agents resulted in enrichment of NCC-like cells. The noradrenergic module was validated by ChIP-seq. Functional studies demonstrated dependency of neuroblastoma with noradrenergic identity on PHOX2B, evocative of lineage addiction. Most neuroblastoma primary tumors express TFs from the noradrenergic and NCC-like modules. Our data demonstrate a previously unknown aspect of tumor heterogeneity relevant for neuroblastoma treatment strategies.

Evaluating hepatocellular carcinoma cell lines for tumour samples using within-sample relative expression orderings of genes.

BACKGROUND & AIMS: Concerns are raised about the representativeness of cell lines for tumours due to the culture environment and misidentification. Liver is a major metastatic destination of many cancers, which might further confuse the origin of hepatocellular carcinoma cell lines. Therefore, it is of crucial importance to understand how well they can represent hepatocellular carcinoma. METHODS: The HCC-specific gene pairs with highly stable relative expression orderings in more than 99% of hepatocellular carcinoma but with reversed relative expression orderings in at least 99% of one of the six types of cancer, colorectal carcinoma, breast carcinoma, non-small-cell lung cancer, gastric carcinoma, pancreatic carcinoma and ovarian carcinoma, were identified. RESULTS: With the simple majority rule, the HCC-specific relative expression orderings from comparisons with colorectal carcinoma and breast carcinoma could exactly discriminate primary hepatocellular carcinoma samples from both primary colorectal carcinoma and breast carcinoma samples. Especially, they correctly classified more than 90% of liver metastatic samples from colorectal carcinoma and breast carcinoma to their original tumours. Finally, using these HCC-specific relative expression orderings from comparisons with six cancer types, we identified eight of 24 hepatocellular carcinoma cell lines in the Cancer Cell Line Encyclopedia (Huh-7, Huh-1, HepG2, Hep3B, JHH-5, JHH-7, C3A and Alexander cells) that are highly representative of hepatocellular carcinoma. Evaluated with a REOs-based prognostic signature for hepatocellular carcinoma, all these eight cell lines showed the same metastatic properties of the high-risk metastatic hepatocellular carcinoma tissues. CONCLUSIONS: Caution should be taken for using hepatocellular carcinoma cell lines. Our results should be helpful to select proper hepatocellular carcinoma cell lines for biological experiments.

Comprehensive genomic characterization of five canine lymphoid tumor cell lines.

BACKGROUND: Leukemia/lymphoma cell lines have been critical in the investigation of the pathogenesis and therapy of hematological malignancies. While human LL cell lines have generally been found to recapitulate the primary tumors from which they were derived, appropriate characterization including cytogenetic and transcriptional assessment is crucial for assessing their clinical predictive value. RESULTS: In the following study, five canine LL cell lines, CLBL-1, Ema, TL-1 (Nody-1), UL-1, and 3132, were characterized using extensive immunophenotyping, karyotypic analysis, oligonucleotide array comparative genomic hybridization (oaCGH), and gene expression profiling. Genome-wide DNA copy number data from the cell lines were also directly compared with 299 primary canine round cell tumors to determine whether the cell lines represent primary tumors, and, if so, what subtype each most closely resembled. CONCLUSIONS: Based on integrated analyses, CLBL-1 was classified as B-cell lymphoma, Ema and TL-1 as T-cell lymphoma, and UL-1 as T-cell acute lymphoblastic leukemia. 3132, originally classified as a B-cell lymphoma, was reclassified as a histiocytic sarcoma based on characteristic cytogenomic properties. In combination, these data begin to elucidate the clinical predictive value of these cell lines which will enhance the appropriate selection of in vitro models for future studies of canine hematological malignancies.

Deep Learning in Label-free Cell Classification.

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

A simple engineered platform reveals different modes of tumor-microenvironmental cell interaction.

How metastatic cancer lesions survive and grow in secondary locations is not fully understood. There is a growing appreciation for the importance of tumor components, i.e. microenvironmental cells, in this process. Here, we used a simple microfabricated dual cell culture platform with a 500 µm gap to assess interactions between two different metastatic melanoma cell lines (1205Lu isolated from a lung lesion established through a mouse xenograft; and WM852 derived from a stage III metastatic lesion of skin) and microenvironmental cells derived from either skin (fibroblasts), lung (epithelial cells) or liver (hepatocytes). We observed differential bi-directional migration between microenvironmental cells and melanoma, depending on the melanoma cell line. Lung epithelial cells and skin fibroblasts, but not hepatocytes, stimulated higher 1205Lu migration than without microenvironmental cells; in the opposite direction, 1205Lu cells induced hepatocytes to migrate, but had no effect on skin fibroblasts and slightly inhibited lung epithelial cells. In contrast, none of the microenvironments had a significant effect on WM852; in this case, skin fibroblasts and hepatocytes--but not lung epithelial cells--exhibited directed migration toward WM852. These observations reveal significant effects a given microenvironmental cell line has on the two different melanoma lines, as well as how melanoma effects different microenvironmental cell lines. Our simple platform thus has potential to provide complex insights into different strategies used by cancerous cells to survive in and colonize metastatic sites.

Sparse coding induced transfer learning for HEp-2 cell classification.

Automated human larynx carcinoma (HEp-2) cell classification is critical for medical diagnosis. In this paper, we propose a sparse coding-based unsupervised transfer learning method for HEp-2 cell classification. First, the low level image feature is extracted for visual representation. Second, a sparse coding scheme with the Elastic Net penalized convex objective function is proposed for unsupervised feature learning. At last, a Support Vector Machine classifier is utilized for model learning and predicting. To our knowledge, this work is the first to transfer the human-crafted visual feature, sensitive to the variation of appearance and shape during cell movement, to the high level representation which directly denotes the correlation of one sample and the bases in the learnt dictionary. Therefore, the proposed method can overcome the difficulty in discriminative feature formulation for different kinds of cells with irregular and changing visual patterns. Large scale comparison experiments will be conducted to show the superiority of this method.