RefCell: Multi-dimensional analysis of image-based high-throughput screens based on ‘typical cells’

Background Image-based high-throughput screening (HTS) reveals a high level of heterogeneity in single cells and multiple cellular states may be observed within a single population. Cutting-edge high-dimensional analysis methods are successful in characterizing cellular heterogeneity, but they suffer from the “curse of dimensionality” and non-standardized outputs. Results Here we introduce RefCell, a multi-dimensional analysis pipeline for image-based HTS that reproducibly captures cells with typical combinations of features in reference states, and uses these “typical cells” as a reference for classification and weighting of metrics. RefCell quantitatively assesses the heterogeneous deviations from typical behavior for each analyzed perturbation or sample. Conclusions We apply RefCell to the analysis of data from a high-throughput imaging screen of a library of 320 ubiquitin protein targeted siRNAs selected to gain insights into the mechanisms of premature aging (progeria). RefCell yields results comparable to a more complex clustering based single cell analysis method, which both reveal more potential hits than conventional average based analysis.

[1]  Yosef Gruenbaum,et al.  Accumulation of mutant lamin A causes progressive changes in nuclear architecture in Hutchinson–Gilford progeria syndrome , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[2]  T. Misteli,et al.  Reversal of the cellular phenotype in the premature aging disease Hutchinson-Gilford progeria syndrome , 2005, Nature Medicine.

[3]  T. Misteli,et al.  A high-content imaging-based screening pipeline for the systematic identification of anti-progeroid compounds. , 2016, Methods.

[4]  B. Kennedy,et al.  Werner and Hutchinson–Gilford progeria syndromes: mechanistic basis of human progeroid diseases , 2007, Nature Reviews Molecular Cell Biology.

[5]  Jonathan Goldstein,et al.  When Is ''Nearest Neighbor'' Meaningful? , 1999, ICDT.

[6]  L. Holmberg,et al.  Quantification of Normal Cell Fraction and Copy Number Neutral LOH in Clinical Lung Cancer Samples Using SNP Array Data , 2009, PloS one.

[7]  Eamonn J. Keogh.  Nearest Neighbor , 2010, Encyclopedia of Machine Learning.

[8]  Lani F. Wu,et al.  Characterizing heterogeneous cellular responses to perturbations , 2008, Proceedings of the National Academy of Sciences.

[9]  Karol Kozak,et al.  Data Mining Techniques in High Content Screening: A Survey , 2009 .

[10]  P. Liberali,et al.  Single-cell and multivariate approaches in genetic perturbation screens , 2014, Nature Reviews Genetics.

[11]  Francis S. Collins,et al.  Human laminopathies: nuclei gone genetically awry , 2006, Nature Reviews Genetics.

[12]  A. Garnier,et al.  Hutchinson–Gilford progeria syndrome as a model for vascular aging , 2015, Biogerontology.

[13]  James Inglese,et al.  Reporting data from high-throughput screening of small-molecule libraries. , 2007, Nature chemical biology.

[14]  Anne E Carpenter,et al.  CellProfiler: image analysis software for identifying and quantifying cell phenotypes , 2006, Genome Biology.

[15]  Pauli Rämö,et al.  CellClassifier: supervised learning of cellular phenotypes , 2009, Bioinform..

[16]  Y. Zou,et al.  DNA damage responses in progeroid syndromes arise from defective maturation of prelamin A , 2006, Journal of Cell Science.

[17]  Erik Meijering,et al.  Imagining the future of bioimage analysis , 2016, Nature Biotechnology.

[18]  Jeff Kiefer,et al.  High-throughput siRNA screening as a method of perturbation of biological systems and identification of targeted pathways coupled with compound screening. , 2009, Methods in molecular biology.

[19]  Aideen Long,et al.  Statistical methods for analysis of high-throughput RNA interference screens , 2009, Nature Methods.

[20]  Anne E Carpenter,et al.  Methods for High-Content , High-Throughput Image-Based Cell Screening , 2006 .

[21]  Karen N Conneely,et al.  Inhibiting farnesylation of progerin prevents the characteristic nuclear blebbing of Hutchinson-Gilford progeria syndrome. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Lani F. Wu,et al.  Multidimensional Drug Profiling By Automated Microscopy , 2004, Science.

[23]  W. Philips,et al.  Accurate Detection of Dysmorphic Nuclei Using Dynamic Programming and Supervised Classification , 2017, PloS one.

[24]  Guanghua Xiao,et al.  iScreen: Image-Based High-Content RNAi Screening Analysis Tools. , 2015, Journal of biomolecular screening.

[25]  Polina Golland,et al.  Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning , 2009, Proceedings of the National Academy of Sciences.

[26]  Guenther Walther,et al.  Science not art: statistically sound methods for identifying subsets in multi-dimensional flow and mass cytometry data sets , 2017, Nature Reviews Immunology.

[27]  T. Misteli,et al.  Repression of the Antioxidant NRF2 Pathway in Premature Aging , 2016, Cell.

[28]  Lani F. Wu,et al.  Cellular Heterogeneity: Do Differences Make a Difference? , 2010, Cell.

[29]  Thomas Wild,et al.  Machine Learning Improves the Precision and Robustness of High-Content Screens , 2011, Journal of biomolecular screening.

[30]  Aabid Shariff,et al.  Automated Image Analysis for High-Content Screening and Analysis , 2010, Journal of biomolecular screening.

[31]  Jan Lammerding,et al.  Nuclear mechanics in disease. , 2011, Annual review of biomedical engineering.

[32]  Wolfgang Losert,et al.  From Cellular Characteristics to Disease Diagnosis: Uncovering Phenotypes with Supercells , 2013, PLoS Comput. Biol..

[33]  F. Collins,et al.  Progerin and telomere dysfunction collaborate to trigger cellular senescence in normal human fibroblasts. , 2011, The Journal of clinical investigation.

[34]  C. Bakal,et al.  Genomic screening with RNAi: results and challenges. , 2010, Annual review of biochemistry.

[35]  Wolfgang Losert,et al.  Automated image analysis of nuclear shape: What can we learn from a prematurely aged cell? , 2012, Aging.

[36]  Lani F. Wu,et al.  Image-based multivariate profiling of drug responses from single cells , 2007, Nature Methods.

[37]  Anne Kümmel,et al.  Comparison of Multivariate Data Analysis Strategies for High-Content Screening , 2011, Journal of biomolecular screening.

[38]  C B Harley,et al.  Telomere length predicts replicative capacity of human fibroblasts. , 1992, Proceedings of the National Academy of Sciences of the United States of America.