Article
Biochemical Research Methods
Minji Jeon, Zhuorui Xie, John E. Evangelista, Megan L. Wojciechowicz, Daniel J. B. Clarke, Avi Ma'ayan
Summary: The L1000 technology is a cost-effective high-throughput transcriptomics technology that provides valuable data for discovering drug and target candidates and inferring mechanisms of action for small molecules. With the use of a deep learning model, L1000 profiles can be transformed to RNA-seq-like profiles, improving the coverage for gene expression analysis. The two-step model achieves high correlation coefficients and low root mean square errors when tested on paired L1000/RNA-seq datasets.
BMC BIOINFORMATICS
(2022)
Article
Multidisciplinary Sciences
Pierre Boyeau, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, Romain Lopez, Nir Yosef
Summary: Detecting differentially expressed genes in scRNA-seq data is challenging due to technical factors. This study presents lvm-DE, a Bayesian approach that utilizes uncertainty from deep generative models to control for effect size and false discovery rate. lvm-DE outperforms existing methods in estimating log fold change and detecting differentially expressed genes.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2023)
Article
Biology
Colleen M. Palmateer, Catherina Artikis, Savannah G. Brovero, Benjamin Friedman, Alexis Gresham, Michelle N. Arbeitman
Summary: Reproductive behaviors in Drosophila melanogaster are controlled by fruitless neurons. Single-cell RNA sequencing on pupal neurons revealed sex-specific gene expression patterns, with over 113 distinct clusters identified. Marker gene analyses showed enrichment of specific functional groups, including circadian clock neurons, mushroom body Kenyon cells, neurotransmitter-producing neurons, and those expressing doublesex. The presence of both male and female neurons in most clusters suggests common gene expression programs, with sex-specific differences overlaying these programs to generate diverse behaviors.
Article
Biochemistry & Molecular Biology
Mengting Huang, Yixuan Yang, Xingzhao Wen, Weiqiang Xu, Na Lu, Xiao Sun, Jing Tu, Zuhong Lu
Summary: Although single cell RNA sequencing technologies are well developed, acquiring large-scale single cell expression data can still be costly. The study proposes a method of compressing expression profiles from the sample dimension by assigning each cell into multiple pools and demonstrates that expression profiles can be inferred from pool expression data with a overlapping pooling design and compressed sensing strategy. This approach, when combined with plate-based scRNA-seq measurement, maintains superior gene detection sensitivity and individual identity while reducing library costs by half.
NUCLEIC ACIDS RESEARCH
(2021)
Article
Multidisciplinary Sciences
Richard C. Tyser, Elmir Mahammadov, Shota Nakanoh, Ludovic Vallier, Antonio Scialdone, Shankar Srinivas
Summary: The single-cell transcriptional profile of a human embryo between 16 and 19 days after fertilization shows similarities and differences in gastrulation compared to mouse and non-human primate models. This study provides new insights into human development and offers valuable information for directed differentiation of human cells in vitro.
Article
Biochemistry & Molecular Biology
Yuansheng Zhang, Dong Zou, Tongtong Zhu, Tianyi Xu, Ming Chen, Guangyi Niu, Wenting Zong, Rong Pan, Wei Jing, Jian Sang, Chang Liu, Yujia Xiong, Yubin Sun, Shuang Zhai, Huanxin Chen, Wenming Zhao, Jingfa Xiao, Yiming Bao, Lili Hao, Zhang Zhang
Summary: GEN is an open-access data portal that integrates a vast amount of transcriptomic profiles, including bulk and single-cell RNA sequencing datasets, with abundant gene annotations and online data analysis services. The website also provides opportunities for integrative analysis at both transcriptional and post-transcriptional levels.
NUCLEIC ACIDS RESEARCH
(2022)
Article
Multidisciplinary Sciences
Kai Battenberg, S. Thomas Kelly, Radu Abu Ras, Nicola A. Hetherington, Makoto Hayashi, Aki Minoda
Summary: Single-cell RNA-sequencing analysis has gained popularity, and UniverSC is a universal tool for processing single-cell RNA-seq data on any platform. It provides a command-line tool, docker image, and containerized graphical application for consistent and comprehensive integration, comparison, and evaluation of data from various platforms. Additionally, a cross-platform application with a graphical user interface is available to address the bottleneck of data processing for researchers without bioinformatics expertise.
NATURE COMMUNICATIONS
(2022)
Article
Biochemical Research Methods
Malte D. Luecken, M. Buettner, K. Chaichoompu, A. Danese, M. Interlandi, M. F. Mueller, D. C. Strobl, L. Zappia, M. Dugas, M. Colome-Tatche, Fabian J. Theis
Summary: This study benchmarked 68 method and preprocessing combinations on 85 batches of gene expression data, highlighting the importance of highly variable gene selection in improving method performance. When dealing with complex integration tasks, scANVI, Scanorama, scVI, and scGen consistently performed well, while the performance of single-cell ATAC-sequencing integration was strongly influenced by the choice of feature space.
Article
Biochemistry & Molecular Biology
Jacob C. Kimmel, David R. Kelley
Summary: scNym is a semi-supervised adversarial neural network that can transfer cell identity annotations between different experiments by learning rich representations of cell identities from both labeled and unlabeled datasets. It shows superior performance in transferring annotations across experiments and can synthesize information from multiple datasets to improve accuracy. Additionally, scNym models are well calibrated, interpretable, and can be enhanced with saliency methods.
Article
Biochemistry & Molecular Biology
Mengwei Li, Xiaomeng Zhang, Kok Siong Ang, Jingjing Ling, Raman Sethi, Nicole Yee Shin Lee, Florent Ginhoux, Jinmiao Chen
Summary: DISCO is an integrated database of single-cell omics data, offering an integrated cell atlas and harmonized metadata that users can utilize for comprehensive single-cell data analysis and exploration.
NUCLEIC ACIDS RESEARCH
(2022)
Article
Biochemical Research Methods
Alexander Gerniers, Orian Bricard, Pierre Dupont
Summary: This study presents a data mining method, MicroCellClust, to identify small subpopulations of cells with highly specific expression profiles. Through controlled experiments, it is shown to achieve a high F-1 score in identifying rare subpopulations of human T cells, specific CD4 T cells from breast cancer samples, and a subpopulation related to a specific stage in the cell cycle. Additionally, three rare subpopulations in mouse embryonic stem cells are successfully identified with MicroCellClust, demonstrating its effectiveness in identifying small subsets of cells with highly specific expression profiles.
Article
Biochemical Research Methods
Hang Hu, Zhong Li, Xiangjie Li, Minzhe Yu, Xiutao Pan
Summary: This study proposes a novel deep embedding clustering method for single-cell RNA-seq data, which integrates deep learning and convolutional autoencoder for feature representation and utilizes a regularized soft K-means algorithm for clustering. Experimental results demonstrate that this method outperforms other approaches in various datasets and exhibits good compatibility and robustness.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Multidisciplinary Sciences
Hui Li, Cory R. Brouwer, Weijun Luo
Summary: Single cell RNA sequencing is widely used in biomedical research, generating large volumes of diverse data. To clean the data from noise and artifacts, researchers have developed the AutoClass model, which integrates an autoencoder and a classifier to effectively remove various types of noise and artifacts.
NATURE COMMUNICATIONS
(2022)
Article
Biochemistry & Molecular Biology
Matthias Flotho, Jeremy Amand, Pascal Hirsch, Friederike Grandke, Tony Wyss-Coray, Andreas Keller, Fabian Kern
Summary: The molecular causes and mechanisms of neurodegenerative diseases are still not well understood. This study introduces ZEBRA, a large single-cell and single-nucleus RNA-seq database, which integrates gene expression and metadata from multiple studies to enhance our understanding of mammalian brain function during aging and disease.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Biochemical Research Methods
Ye Yuan, Ziv Bar-Joseph
Summary: Time-course gene-expression data have been widely used to infer gene regulatory and signaling relationships. This study developed deep learning methods for predicting gene interactions from single cell RNA-Seq data, showing improved accuracy over prior methods and potential for assigning new gene functions.
BRIEFINGS IN BIOINFORMATICS
(2021)
Letter
Biotechnology & Applied Microbiology
Orit Rozenblatt-Rosen, Jay W. Shin, Jennifer E. Rood, Anna Hupalowska, Aviv Regev, Holger Heyn
NATURE BIOTECHNOLOGY
(2021)
Article
Multidisciplinary Sciences
Karen O. Dixon, Marcin Tabaka, Markus A. Schramm, Sheng Xiao, Ruihan Tang, Danielle Dionne, Ana. C. Anderson, Orit Rozenblatt-Rosen, Aviv Regev, Vijay K. Kuchroo
Summary: Research demonstrates the crucial role of TIM-3 in regulating dendritic cell (DC) function, with TIM-3 deletion promoting strong anti-tumour immunity by preventing DCs from expressing a regulatory program and facilitating the maintenance of CD8(+) effector and stem-like T cells. The absence of TIM-3 in DCs leads to increased accumulation of reactive oxygen species, resulting in NLRP3 inflammasome activation, and inhibition of inflammasome activation or downstream effector cytokines IL-1 beta and IL-18 abrogates the protective anti-tumour immunity observed with TIM-3 deletion in DCs.
Article
Multidisciplinary Sciences
Xun Chen, Matteo Gentili, Nir Hacohen, Aviv Regev
Summary: CeVICA is a cell-free nanobody engineering platform using ribosome display and computational clustering analysis for in vitro selection. It has successfully developed nanobodies against the RBD of SARS-CoV-2 spike protein, with 30 identified as true RBD binders and 11 able to inhibit SARS-CoV-2 pseudotyped virus infection.
NATURE COMMUNICATIONS
(2021)
Correction
Multidisciplinary Sciences
Velina Kozareva, Caroline Martin, Tomas Osorno, Stephanie Rudolph, Chong Guo, Charles Vanderburg, Naeem Nadaf, Aviv Regev, Wade G. Regehr, Evan Macosko
Article
Biotechnology & Applied Microbiology
Evgenij Fiskin, Caleb A. Lareau, Leif S. Ludwig, Gokcen Eraslan, Feimei Liu, Aaron M. Ring, Ramnik J. Xavier, Aviv Regev
Summary: The study introduces a novel method called PHAGE-ATAC for simultaneous single-cell measurements of protein levels and chromatin accessibility profiles, using mitochondrial DNA-based clonal tracing. PHAGE-ATAC is utilized for multimodal analysis in primary human immune cells, sample multiplexing, intracellular protein analysis, and detection of SARS-CoV-2 spike protein. Additionally, a synthetic high-complexity phage library is constructed for selection of antigen-specific nanobodies, enabling protein detection, cell characterization, and screening with single-cell genomics.
NATURE BIOTECHNOLOGY
(2022)
Review
Biotechnology & Applied Microbiology
Giovanni Palla, David S. Fischer, Aviv Regev, Fabian J. Theis
Summary: Methods for profiling RNA and protein expression in a spatially resolved manner have rapidly advanced, but clear articulation of key biological questions and development of computational tools are crucial. Decisions on molecular features and inclusion of cell shape in analysis need to be made by developers. Optimal ways to compare tissue samples at different length scales are still being sought.
NATURE BIOTECHNOLOGY
(2022)
Article
Multidisciplinary Sciences
S. Vickovic, B. Lotstedt, J. Klughammer, S. Mages, A. Segerstolpe, O. Rozenblatt-Rosen, A. Regev
Summary: The spatial organization of cells and molecules is crucial for tissue function and disease. Spatial transcriptomics, a technique for capturing and locating RNA in tissues, has been advanced with the development of a fully automated platform called Spatial Multi-Omics (SM-Omics). SM-Omics combines spatial transcriptomics and antibody-based protein measurement, allowing high-throughput analysis of multiple omics in a short time.
NATURE COMMUNICATIONS
(2022)
Article
Multidisciplinary Sciences
Eeshit Dhaval Vaishnav, Carl G. de Boer, Jennifer Molinet, Moran Yassour, Lin Fan, Xian Adiconis, Dawn A. Thompson, Joshua Z. Levin, Francisco A. Cubillos, Aviv Regev
Summary: This study builds sequence-to-expression models using deep neural networks to analyze the expression levels of promoter DNA sequences in Saccharomyces cerevisiae, and reveals principles of regulatory evolution. The findings show that regulatory evolution is rapid and subject to diminishing returns epistasis, conflicting expression objectives in different environments constrain expression adaptation, and stabilizing selection on gene expression leads to the moderation of regulatory complexity.
Article
Biotechnology & Applied Microbiology
Livnat Jerby-Arnon, Aviv Regev
Summary: DIALOGUE is a method that systematically deciphers the functional interactions of cells in tissues. It identifies coordinated cellular programs in different cell types, forming higher-order functional units at the tissue level. DIALOGUE also predicts disease outcomes and predisposition, and identifies risk genes for various diseases.
NATURE BIOTECHNOLOGY
(2022)
Article
Genetics & Heredity
Karthik A. Jagadeesh, Kushal K. Dey, Daniel T. Montoro, Rahul Mohan, Steven Gazal, Jesse M. Engreitz, Ramnik J. Xavier, Alkes L. Price, Aviv Regev
Summary: The study introduces a new framework called sc-linker, which integrates single-cell RNA sequencing and epigenomic data on the basis of genome-wide association studies to infer the cell types and processes by which genetic variants influence disease.
Article
Biochemistry & Molecular Biology
Jennifer E. Rood, Aidan Maartens, Anna Hupalowska, Sarah A. Teichmann, Aviv Regev
Summary: Single-cell atlases have the potential to bridge the gap between genes, diseases, and therapies. By understanding disease mechanisms at the cellular and tissue levels, they can aid in disease diagnostics, drug target identification, and the development of new therapies.
Article
Biotechnology & Applied Microbiology
Simon Mages, Noa Moriel, Inbal Avraham-Davidi, Evan Murray, Jan Watter, Fei Chen, Orit Rozenblatt-Rosen, Johanna Klughammer, Aviv Regev, Mor Nitzan
Summary: Transferring annotations of single-cell-, spatial- and multi-omics data is challenging due to technical limitations and biological variations. We present TACCO, a computational framework for annotation transfer, which utilizes continuous mixtures of cells or molecules to annotate a wide variety of data. TACCO achieves high accuracy while reducing computational requirements and scales to larger datasets.
NATURE BIOTECHNOLOGY
(2023)
Article
Biochemical Research Methods
Ethan A. G. Baker, Denis Schapiro, Bianca Dumitrascu, Sanja Vickovic, Aviv Regev
Summary: As the spatially resolved multiplex profiling of RNA and proteins becomes more prominent, understanding the statistical power for testing specific hypotheses in such experiments is crucial. This study introduces a method for generating tunable in silico tissues and constructs a computational framework for spatial power analysis using spatial profiling data sets. The framework can be applied to diverse spatial data modalities and tissues of interest, providing insights for spatial omics studies.
Article
Oncology
Yunpeng Liu-Lupo, James Dongjoo Ham, Swarna K. A. Jeewajee, Lan Nguyen, Toni Delorey, Azucena Ramos, David M. Weinstock, Aviv Regev, Michael T. Hemann
Summary: The study used single-cell RNA-Seq and computational inference to identify key differences between near-haploid and diploid leukemia cells. They found that RAD51B, a component of the homologous recombination pathway, is an essential gene in near-haploid leukemia. Furthermore, RAD51B and its associated genes were overexpressed in near-haploid leukemia patients, suggesting that RAD51B could be a promising target for therapy in this treatment-resistant disease.
BLOOD CANCER JOURNAL
(2023)