A5047608840- Single-cell and spatial transcriptomics
- Gene expression and cancer classification
- Lipid Membrane Structure and Behavior
- Cell Image Analysis Techniques
- Advanced Proteomics Techniques and Applications
- Computational Drug Discovery Methods
- Bacterial Genetics and Biotechnology
- Bioinformatics and Genomic Networks
- Protein Degradation and Inhibitors
- Cancer Cells and Metastasis
- Protein purification and stability
- Radiomics and Machine Learning in Medical Imaging
- Protein Structure and Dynamics
- Ubiquitin and proteasome pathways
- Statistical Methods in Clinical Trials
- Advanced Fluorescence Microscopy Techniques
- Endoplasmic Reticulum Stress and Disease
- Analytical Chemistry and Chromatography
- Forecasting Techniques and Applications
- Biomedical Text Mining and Ontologies
- Bacteriophages and microbial interactions
- Phytochemicals and Antioxidant Activities
- RNA Research and Splicing
- Biosimilars and Bioanalytical Methods
- Gene Regulatory Network Analysis
Center for Systems Biology
2020-2025
Harvard University
2019-2025
Institut de génétique et de biologie moléculaire et cellulaire
2023-2024
Centre National de la Recherche Scientifique
2024
Inserm
2024
University of Kansas
2016-2021
Center for Cancer Research
2021
Advanced solid cancers are complex assemblies of tumor, immune, and stromal cells characterized by high intratumoral variation. We use highly multiplexed tissue imaging, 3D reconstruction, spatial statistics, machine learning to identify cell types states underlying morphological features known diagnostic prognostic significance in colorectal cancer. Quantitation these high-plex marker space reveals recurrent transitions from one tumor morphology the next, some which coincident with...
Highly multiplexed tissue imaging makes detailed molecular analysis of single cells possible in a preserved spatial context. However, reproducible large multichannel images poses substantial computational challenge. Here, we describe modular and open-source pipeline, MCMICRO, for performing the sequential steps needed to transform whole-slide into single-cell data. We demonstrate use MCMICRO on tumor acquired using multiple platforms, thereby providing solid foundation continued development software.
The Library of Integrated Network-based Cellular Signatures (LINCS), an NIH Common Fund program, has cataloged and analyzed cellular function molecular activity profiles in response to >80,000 perturbing agents that are potentially disruptive cells. Because the importance proteins their modifications specific perturbations, four six LINCS centers have included significant proteomics efforts characterization resulting phenotype. This manuscript aims describe this effort data harmonization...
ABSTRACT Highly multiplexed tissue imaging makes molecular analysis of single cells possible in a preserved spatial context. However, reproducible the underlying data poses substantial computational challenge. Here we describe modular and open-source pipeline (MCMICRO) for performing sequential steps needed to transform large, multi-channel whole slide images into single-cell data. We demonstrate use MCMICRO on different tissues tumors acquired using multiple platforms, thereby providing...
SUMMARY Advanced solid cancers are complex assemblies of tumor, immune, and stromal cells characterized by high intratumoral variation. We use highly multiplexed tissue imaging, 3D reconstruction, spatial statistics, machine learning to identify cell types states underlying morphological features known diagnostic prognostic significance in colorectal cancer. Quantitation these high-plex marker space reveals recurrent transitions from one tumor morphology the next, some which coincident with...
We performed quantitative proteomics on 60 human-derived breast cancer cell line models to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. used the identify characterize subtypes showed that they conform known transcriptional subtypes, revealing molecular are preserved even in under-sampled protein feature sets. All freely available as public resources LINCS portal. anticipate these datasets, either isolation or combination...
Type III Secretion Systems (T3SS) are complex bacterial structures that provide gram-negative pathogens with a unique virulence mechanism whereby they grow needle-like structure in order to inject effector proteins into the cytoplasm of host cell. Numerous experiments have been performed understand structural details this nanomachine during past decade. Despite concerted efforts molecular and biologists, several crucial aspects assembly structure, such as regulation length needle itself,...
The true accuracy of a machine-learning model is population-level statistic that cannot be observed directly. In practice, predictor performance estimated against one or more test datasets, and the this estimate strongly depends on how well sets represent all possible unseen datasets. Here we describe paired evaluation as simple, robust approach for evaluating models in small-sample biological clinical studies. We use method to evaluate predictors drug response breast cancer cell lines...
Protein turnover is vital to cellular homeostasis. Many proteins are degraded efficiently only after they have been post-translationally “tagged” with a polyubiquitin chain. Ubiquitylation form of Post-Translational Modification (PTM): addition ubiquitin the chain catalyzed by E3 ligases, and removal De-UBiquitylating enzyme (DUB). Nearly four decades ago, Goldbeter Koshland discovered that reversible PTM cycles function like on-off switches when substrates at saturating concentrations....
Abstract The cell cycle is a tightly regulated process that requires precise temporal expression of hundreds cycledependent genes. However, the genome-wide dynamics mRNA metabolism throughout remain uncharacterized. Here, we combined single-cell multiome sequencing, biophysical modeling, and deep learning to quantify rates transcription, splicing, nuclear export, degradation. Our approach revealed both transcriptional post-transcriptional processes exhibit distinct oscillatory waves at...
Abstract We performed quantitative proteomics on 61 human-derived breast cancer cell lines to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. used the identify characterize subtypes showed that they conform known transcriptional subtypes, revealing molecular are preserved even in under-sampled protein feature sets. All freely available as public resources LINCS portal. anticipate these datasets, either isolation or...
Abstract Protein turnover is vital to protein homeostasis within the cell. Many proteins are degraded efficiently only after they have been post-translationally “tagged” with a polyubiquitin chain. Ubiquitylation form of Post-Translational Modification (PTM): addition ubiquitin chain catalyzed by E3 ligases, and removal De-UBiquitylating enzyme (DUB). Over three decades ago, Goldbeter Koshland discovered that reversible PTM cycles function like on-off switches when substrates at saturating...
Abstract Bacteria construct many structures, like the flagellar hook and type III secretion system, that aid in crucial processes such as locomotion pathogenesis. Experimental work has suggested two competing mechanisms bacteria could use to regulate length these structures: “ruler” mechanism “substrate switching” mechanism. In this work, we constructed a mathematical model of control based on ruler mechanism, found predictions are consistent with experimental data not just for scaling...
ABSTRACT The true accuracy of a machine learning model is population-level statistic that cannot be observed directly. In practice, predictor performance estimated against one or more test datasets, and the this estimate strongly depends on how well sets represent all possible unseen datasets. Here we present paired evaluation, simple approach for increasing robustness evaluation by systematic pairing samples, use it to evaluate predictors drug response in breast cancer cell lines disease...