Vasilevich AS, Carlier A, de Boer J, Singh S. How Not To Drown in Data: A Guide for Biomaterial Engineers. Trends Biotechnol. 2017 Aug;35(8):743–55.
Rohban MH, Singh S, Wu X, Berthet JB, Bray M-A, Shrestha Y, Varelas X, Boehm JS, Carpenter AE. Systematic morphological profiling of human gene and allele function via Cell Painting. Elife [Internet]. 2017 Mar 18;6.
Caicedo JC, et al. Data-analysis strategies for image-based cell profiling. Nat Methods. 2017 Aug 31;14(9):849–63.
Caicedo JC, Singh S, Carpenter AE. Applications in image-based profiling of perturbations. Curr Opin Biotechnol. 2016;39: 134–142.
Bray M-A, Singh S, Han H, et al. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. bioRxiv. 2016. 049817.
Singh S, Carpenter AE, Genovesio A. Increasing the content of high-content screening: An overview. J Biomol Screen. 2014;19:640–650.
Shantanu Singh, Ph.D.
Shantanu Singh is a senior group leader in the Imaging Platform at the Broad Institute. He leads a data science group that develops computational and statistical methods to create fingerprints of genes, chemicals, and diseases from microscopy images of cells. Using assays like Cell Painting that capture a broad range of their morphological properties, cellular populations are characterized at single-cell resolution to discover similarities and differences among treatments. This work has the potential to transform how both the targets and therapies for disease are identified.
After completing his Ph.D. at Ohio State in computer science, Shantanu joined the Imaging Platform, inspired by the group’s vision to make cell morphology as computable as genomes. He has previously worked in research groups at Mercedes-Benz R&D, GE Global Research, and Lawrence Livermore National Laboratory, where he applied computer vision and machine learning techniques to a wide range of problems in road safety, cell biology, and geospatial imaging.