Biology subjects

Chandrasekaran, S. N.

Publications and source records attributed to Chandrasekaran, S. N..

5 recordsLinked to original sources

bioRxiv2023

nELISA: A high-throughput, high-plex platform enables quantitative profiling of the secretome

We present the nELISA, a high-throughput, high-fidelity, and high-plex protein profiling platform. DNA oligonucleotides are used to pre-assemble antibody pairs on spectrally encoded microparticles and perform displacement-mediated detection. Spatial separation between non-cognate antibodies prevents the rise of reagent-driven cross-reactivity, while read-out is performed cost-efficiently and at high-throughput using flow cytometry. nELISA can measure both protein concentration and their post-translational modifications. We assembled an inflammatory panel of 191 targets that were multiplexed without cross-reactivity nor impact on performance vs 1-plex signals, with sensitivities as low as 0.1 pg/mL and measurements spanning 7 orders of magnitude. We then performed a large-scale inflammatory-secretome perturbation screen of peripheral blood mononuclear cells (PBMCs), with cytokines as both perturbagens and read-outs, measuring 7,392 samples and generating [~]1.4M protein data points in under a week; a significant advance in throughput compared to other highly multiplexed immunoassays. We uncovered 447 significant cytokine responses, including multiple putatively novel ones, that were conserved across donors and stimulation conditions. We validate nELISA for phenotypic screening, where its capacity to faithfully report hundreds of proteins make it a powerful tool across multiple stages of drug discovery.

bioengineering↗

bioRxiv2023

JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations

Image-based profiling has emerged as a powerful technology for various steps in basic biological and pharmaceutical discovery, but the community has lacked a large, public reference set of data from chemical and genetic perturbations. Here we present data generated by the Joint Undertaking for Morphological Profiling (JUMP)-Cell Painting Consortium, a collaboration between 10 pharmaceutical companies, six supporting technology companies, and two non-profit partners. When completed, the dataset will contain images and profiles from the Cell Painting assay for over 116,750 unique compounds, over-expression of 12,602 genes, and knockout of 7,975 genes using CRISPR-Cas9, all in human osteosarcoma cells (U2OS). The dataset is estimated to be 115 TB in size and capturing 1.6 billion cells and their single-cell profiles. File quality control and upload is underway and will be completed over the coming months at the Cell Painting Gallery: https://registry.opendata.aws/cellpainting-gallery. A portal to visualize a subset of the data is available at https://phenaid.ardigen.com/jumpcpexplorer/.

bioinformatics↗

bioRxiv2022

Optimizing the Cell Painting assay for image-based profiling

In image-based profiling, software extracts thousands of morphological features of cells from multi-channel fluorescence microscopy images, yielding single-cell profiles that can be used for basic research and drug discovery. Powerful applications have been proven, including clustering chemical and genetic perturbations based on their similar morphological impact, identifying disease phenotypes by observing differences in profiles between healthy and diseased cells, and predicting assay outcomes using machine learning, among many others. Here we provide an updated protocol for the most popular assay for image-based profiling, Cell Painting. Introduced in 2013, it uses six stains imaged in five channels and labels eight diverse components of the cell: DNA, cytoplasmic RNA, nucleoli, actin, Golgi apparatus, plasma membrane, endoplasmic reticulum, and mitochondria. The original protocol was updated in 2016 based on several years experience running it at two sites, after optimizing it by visual stain quality. Here we describe the work of the Joint Undertaking for Morphological Profiling (JUMP) Cell Painting Consortium, aiming to improve upon the assay via quantitative optimization, based on the measured ability of the assay to detect morphological phenotypes and group similar perturbations together. We find that the assay gives very robust outputs despite a variety of changes to the protocol and that two vendors dyes work equivalently well. We present Cell Painting version 3, in which some steps are simplified and several stain concentrations can be reduced, saving costs. Cell culture and image acquisition take 1-2 weeks for a typically sized batch of 20 or fewer plates; feature extraction and data analysis take an additional 1-2 weeks. Key references using this protocolVirtual screening for small-molecule pathway regulators by image-profile matching (https://doi.org/10.1016/j.cels.2022.08.003) - recent work examining the ability to use collected Cell Painting profiles to screen for regulators of a number of diverse biological pathways. JUMP Cell Painting dataset: images and profiles from two billion cells perturbed by 140,000 chemical and genetic perturbations (DOI) - the description of the main JUMP master public data set, using this protocol in the production of >200 TB of image data and >200 TB of measured profiles. Key data used in this protocolCell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes (https://doi.org/10.1038/nprot.2016.105) - this paper provides the first step-by-step Cell Painting protocol ever released.

bioinformatics↗

bioRxiv2022

Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations

Identifying genetic and chemical perturbations with similar impacts on cell morphology can reveal compounds mechanisms of action or novel regulators of genetic pathways. Research on methods for identifying such similarities has lagged due to a lack of carefully designed and well-annotated image sets of cells treated with chemical and genetic perturbations. Here, we create such a Resource dataset, CPJUMP1, where each perturbed gene is a known target of at least two chemical compounds in the dataset. We systematically explore the directionality of correlations among perturbations that target the same gene, and we find that identifying matches between chemical perturbations and genetic perturbations is a challenging task. Our dataset and baseline analyses provide a benchmark for evaluating methods that measure perturbation similarities and impact, and more generally, learn effective representations of cellular state from microscopy images. Such advancements would accelerate the applications of image-based profiling, such as functional genomics and drug discovery.

bioinformatics↗

bioRxiv2019

Thermodynamic impacts of combinatorial mutagenesis on protein conformational stability: precise, high-throughput measurement by Thermofluor

The D1 switch is a packing motif, broadly distributed in the proteome, that couples tryptophanyl-tRNA synthetase (TrpRS) domain movement to catalysis and specificity, thereby creating an escapement mechanism essential to free-energy transduction. The escapement mechanism arose from analysis of an extensive set of combinatorial mutations to this motif, which allowed us to relate mutant-induced changes quantitatively to both kinetic and computational parameters during catalysis. To further characterize the origins of this escapement mechanism in differential TrpRS conformational stabilities, we use high-throughput Thermofluor measurements for the 16 variants to extend analysis of the mutated residues to their impact on unliganded TrpRS stability. Aggregation of denatured proteins complicates thermodynamic interpretations of denaturation experiments. The free energy landscape of a liganded TrpRS complex, carried out for different purposes, closely matches the volume, helix content, and transition temperatures of Thermoflour and CD melting profiles. Regression analysis using the combinatorial design matrix accounts for >90% of the variance in Tms of both Thermofluor and CD melting profiles. We argue that the agreement of experimental melting temperatures with both computational free energy landscape and with Regression modeling means that experimental melting profiles can be used to analyze the thermodynamic impact of combinatorial mutations. Tertiary packing and aromatic stacking of Phenylalanine 37 exerts a dominant stabilizing effect on both native and molten globular states. The TrpRS Urzyme structure remains essentially intact at the highest temperatures explored by the simulations.

biophysics↗