Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors

Tailoring the best treatments to cancer patients is an important open challenge. Here, we build a precision oncology data science and software framework for PERsonalized single-Cell Expression-based Planning for Treatments In Oncology (PERCEPTION). Our approach capitalizes on recently published matched bulk and single-cell transcriptome profiles of large-scale cell-line drug screens to build treatment response models from patient single-cell (SC) tumor transcriptomics. First, we show that PERCEPTION successfully predicts the response to monotherapy and combination treatments in screens performed in cancer and patient-tumor-derived primary cells based on SC-expression profiles. Second, it successfully stratifies responders to combination therapy based on the patient tumor SC-expression in two very recent multiple myeloma and breast cancer clinical trials. Thirdly, it captures the development of clinical resistance to five standard tyrosine kinase inhibitors using tumor SC-expression profiles obtained during treatment in a lung cancer patient cohort. Notably, PERCEPTION outperforms state-of-the-art bulk expression-based predictors in all three clinical cohorts. In sum, this study provides a first-of-its-kind conceptual and computational method that is predictive of response to therapy in patients, based on the clonal SC gene expression of their tumors.

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The 3D Genome Structure of Single Cells

Annual Review of Biomedical Data Science ◽

10.1146/annurev-biodatasci-020121-084709 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Tianming Zhou ◽

Ruochi Zhang ◽

Jian Ma

Keyword(s):

Single Cell ◽

Data Science ◽

Spatial Organization ◽

Method Development ◽

Single Cells ◽

Genome Structure ◽

Computational Method ◽

Publication Date ◽

3D Genome ◽

Genome Features

The spatial organization of the genome in the cell nucleus is pivotal to cell function. However, how the 3D genome organization and its dynamics influence cellular phenotypes remains poorly understood. The very recent development of single-cell technologies for probing the 3D genome, especially single-cell Hi-C (scHi-C), has ushered in a new era of unveiling cell-to-cell variability of 3D genome features at an unprecedented resolution. Here, we review recent developments in computational approaches to the analysis of scHi-C, including data processing, dimensionality reduction, imputation for enhancing data quality, and the revealing of 3D genome features at single-cell resolution. While much progress has been made in computational method development to analyze single-cell 3D genomes, substantial future work is needed to improve data interpretation and multimodal data integration, which are critical to reveal fundamental connections between genome structure and function among heterogeneous cell populations in various biological contexts. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

Genome Medicine ◽

10.1186/s13073-021-01000-y ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Chayaporn Suphavilai ◽

Shumei Chia ◽

Ankur Sharma ◽

Lorna Tu ◽

Rafael Peres Da Silva ◽

...

Keyword(s):

Single Cell ◽

Large Scale ◽

Drug Response ◽

Drug Repurposing ◽

High Accuracy ◽

Molecular Heterogeneity ◽

Precision Oncology ◽

Scale Analysis ◽

Rna Seq ◽

Large Scale Analysis

AbstractWhile understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc’s monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc.

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M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data

BMC Bioinformatics ◽

10.1186/s12859-019-3243-1 ◽

2019 ◽

Vol 20 (S24) ◽

Cited By ~ 1

Author(s):

Yu Zhang ◽

Changlin Wan ◽

Pengcheng Wang ◽

Wennan Chang ◽

Yan Huo ◽

...

Keyword(s):

Gene Expression ◽

Model Selection ◽

Statistical Model ◽

Single Cell ◽

Differential Gene Expression ◽

Large Scale ◽

Expression Profiles ◽

Sequencing Data ◽

Differential Gene ◽

Selection Of

Abstract Background Various statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model. Results We developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model. Conclusion A comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S.

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A map of tumor–host interactions in glioma at single-cell resolution

GigaScience ◽

10.1093/gigascience/giaa109 ◽

2020 ◽

Vol 9 (10) ◽

Cited By ~ 3

Author(s):

Francesca Pia Caruso ◽

Luciano Garofano ◽

Fulvio D'Angelo ◽

Kai Yu ◽

Fuchou Tang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cross Talk ◽

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Sequencing Data ◽

Host Interaction ◽

Receptor Interactions ◽

Single Cell Rna Sequencing

ABSTRACT Background Single-cell RNA sequencing is the reference technique for characterizing the heterogeneity of the tumor microenvironment. The composition of the various cell types making up the microenvironment can significantly affect the way in which the immune system activates cancer rejection mechanisms. Understanding the cross-talk signals between immune cells and cancer cells is of fundamental importance for the identification of immuno-oncology therapeutic targets. Results We present a novel method, single-cell Tumor–Host Interaction tool (scTHI), to identify significantly activated ligand–receptor interactions across clusters of cells from single-cell RNA sequencing data. We apply our approach to uncover the ligand–receptor interactions in glioma using 6 publicly available human glioma datasets encompassing 57,060 gene expression profiles from 71 patients. By leveraging this large-scale collection we show that unexpected cross-talk partners are highly conserved across different datasets in the majority of the tumor samples. This suggests that shared cross-talk mechanisms exist in glioma. Conclusions Our results provide a complete map of the active tumor–host interaction pairs in glioma that can be therapeutically exploited to reduce the immunosuppressive action of the microenvironment in brain tumor.

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Real age prediction from the transcriptome with RAPToR

10.1101/2021.09.07.459270 ◽

2021 ◽

Author(s):

Romain Bulteau ◽

Mirko Francesconi

Keyword(s):

Gene Expression ◽

Large Scale ◽

Time Series Data ◽

Expression Profiles ◽

Computational Method ◽

Model Systems ◽

Developmental Expression ◽

Model Organisms ◽

Series Data ◽

Developmental Variation

AbstractGenome-wide gene expression profiling is a powerful tool for exploratory analyses, providing a high dimensional picture of the state of a biological system. However, uncontrolled variation among samples can obscure and confound the effect of variables of interest. Uncontrolled developmental variation is often a major source of unknown expression variation in developmental systems. Existing methods to sort samples from transcriptomes require many samples to infer developmental trajectories and only provide a relative pseudo-time.Here we present RAPToR (Real Age Prediction from Transcriptome staging on Reference), a simple computational method to estimate the absolute developmental age of even a single sample from its gene expression with up to minutes precision. We achieve this by staging samples on high-resolution reference developmental expression profiles we build from existing time series data. We implemented RAPToR for the most common animal model systems: nematode, fruit fly, zebrafish, and mouse, and demonstrate application for non-model organisms. We show how developmental variation discovered by RAPToR can be exploited to increase power to detect differential expression and to untangle the signal of perturbations of interest even when it is completely confounded with development. We anticipate our RAPToR post-profiling staging strategy will be especially useful in large scale single organism profiling because it eliminates the need for synchronization or for a tedious and potentially difficult step of accurate staging before profiling.

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Single-Cell RNA Sequencing Elucidates the Structure and Organization of Microbial Communities

Frontiers in Microbiology ◽

10.3389/fmicb.2021.713128 ◽

2021 ◽

Vol 12 ◽

Author(s):

Melanie A. Brennan ◽

Adam Z. Rosenthal

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Expression Profiles ◽

Human Life ◽

Bacterial Species ◽

Population Heterogeneity ◽

Specific Gene ◽

Valuable Insight ◽

Single Cell Rna Sequencing

Clonal bacterial populations exhibit various forms of heterogeneity, including co-occurrence of cells with different morphological traits, biochemical properties, and gene expression profiles. This heterogeneity is prevalent in a variety of environments. For example, the productivity of large-scale industrial fermentations and virulence of infectious diseases are shaped by cell population heterogeneity and have a direct impact on human life. Due to the need and importance to better understand this heterogeneity, multiple methods of examining single-cell heterogeneity have been developed. Traditionally, fluorescent reporters or probes are used to examine a specific gene of interest, providing a useful but inherently biased approach. In contrast, single-cell RNA sequencing (scRNA-seq) is an agnostic approach to examine heterogeneity and has been successfully applied to eukaryotic cells. Unfortunately, current extensively utilized methods of eukaryotic scRNA-seq present difficulties when applied to bacteria. Specifically, bacteria have a cell wall which makes eukaryotic lysis methods incompatible, bacterial mRNA has a shorter half-life and lower copy numbers, and isolating an individual bacterial species from a mixed community is difficult. Recent work has demonstrated that these technical hurdles can be overcome, providing valuable insight into factors influencing microbial heterogeneity. This perspective describes the emerging microbial scRNA-seq toolkit. We outline the benefit of these new tools in elucidating numerous scientific questions in microbiological studies and offer insight about the possible rules that govern the segregation of traits in individual microbial cells.

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Single-cell RNA sequencing of adultDrosophilaovary identifies transcriptional programs governing oogenesis

10.1101/802314 ◽

2019 ◽

Author(s):

Allison Jevitt ◽

Deeptiman Chatterjee ◽

Gengqiang Xie ◽

Xian-Feng Wang ◽

Taylor Otwell ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Developmental Process ◽

Expression Profiles ◽

Follicle Cell ◽

Cell Types ◽

Broad Perspective ◽

Single Cell Rna Sequencing ◽

Egg Shell

AbstractOogenesis is a complex developmental process that involves spatiotemporally regulated coordination between the germline and supporting, somatic cell populations. This process has been modelled extensively using theDrosophilaovary. While different ovarian cell types have been identified through traditional means, the large-scale expression profiles underlying each cell type remain unknown. Using single-cell RNA sequencing technology, we have built a transcriptomic dataset for the adultDrosophilaovary and connected tissues. This dataset captures the entire transcriptional trajectory of the developing follicle cell population over time. Our findings provide detailed insight into processes such as cell-cycle switching, migration, symmetry breaking, nurse cell engulfment, egg-shell formation, and signaling during corpus luteum formation, marking a newly identified oogenesis-to-ovulation transition. Altogether, these findings provide a broad perspective on oogenesis at a single-cell resolution while revealing new genetic markers and fate-specific transcriptional signatures to facilitate future studies.

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Deep soft K-means clustering with self-training for single-cell RNA sequence data

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqaa039 ◽

2020 ◽

Vol 2 (2) ◽

Cited By ~ 2

Author(s):

Liang Chen ◽

Weinan Wang ◽

Yuyao Zhai ◽

Minghua Deng

Keyword(s):

Deep Learning ◽

Single Cell ◽

Large Scale ◽

Sequence Data ◽

Dimensional Space ◽

Expression Profiles ◽

Single Cells ◽

Clustering Algorithms ◽

Training Procedure ◽

Latent Space

Abstract Single-cell RNA sequencing (scRNA-seq) allows researchers to study cell heterogeneity at the cellular level. A crucial step in analyzing scRNA-seq data is to cluster cells into subpopulations to facilitate subsequent downstream analysis. However, frequent dropout events and increasing size of scRNA-seq data make clustering such high-dimensional, sparse and massive transcriptional expression profiles challenging. Although some existing deep learning-based clustering algorithms for single cells combine dimensionality reduction with clustering, they either ignore the distance and affinity constraints between similar cells or make some additional latent space assumptions like mixture Gaussian distribution, failing to learn cluster-friendly low-dimensional space. Therefore, in this paper, we combine the deep learning technique with the use of a denoising autoencoder to characterize scRNA-seq data while propose a soft self-training K-means algorithm to cluster the cell population in the learned latent space. The self-training procedure can effectively aggregate the similar cells and pursue more cluster-friendly latent space. Our method, called ‘scziDesk’, alternately performs data compression, data reconstruction and soft clustering iteratively, and the results exhibit excellent compatibility and robustness in both simulated and real data. Moreover, our proposed method has perfect scalability in line with cell size on large-scale datasets.

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GapClust is a light-weight approach distinguishing rare cells from voluminous single cell expression profiles

Nature Communications ◽

10.1038/s41467-021-24489-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Botao Fa ◽

Ting Wei ◽

Yuan Zhou ◽

Luke Johnston ◽

Xin Yuan ◽

...

Keyword(s):

Single Cell ◽

Large Scale ◽

State Of The Art ◽

Expression Profiles ◽

Cell Types ◽

Superior Performance ◽

Light Weight ◽

Memory Efficiency ◽

Rare Cells ◽

Cell Expression

AbstractSingle cell RNA sequencing (scRNA-seq) is a powerful tool in detailing the cellular landscape within complex tissues. Large-scale single cell transcriptomics provide both opportunities and challenges for identifying rare cells playing crucial roles in development and disease. Here, we develop GapClust, a light-weight algorithm to detect rare cell types from ultra-large scRNA-seq datasets with state-of-the-art speed and memory efficiency. Benchmarking on diverse experimental datasets demonstrates the superior performance of GapClust compared to other recently proposed methods. When applying our algorithm to an intestine and 68 k PBMC datasets, GapClust identifies the tuft cells and a previously unrecognised subtype of monocyte, respectively.

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Massive single-cell RNA-seq analysis and imputation via deep learning

10.1101/315556 ◽

2018 ◽

Cited By ~ 9

Author(s):

Yue Deng ◽

Feng Bao ◽

Qionghai Dai ◽

Lani F. Wu ◽

Steven J. Altschuler

Keyword(s):

Deep Learning ◽

Single Cell ◽

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Rna Seq ◽

Fine Grained ◽

Cell Type Composition ◽

Type Composition ◽

Cell Gene Expression

Recent advances in large-scale single cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states within heterogeneous tissues. We present scScope, a scalable deep-learning based approach that can accurately and rapidly identify cell-type composition from millions of noisy single-cell gene-expression profiles.

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