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Article
Biotechnology & Applied Microbiology
Yingxin Lin et al.
Summary: This study introduces a transfer learning method called scJoint to integrate different single-cell transcriptome and chromatin accessibility sequencing datasets. By training labeled and unlabeled data using a neural network in a semi-supervised learning framework, scJoint enables label transfer and joint visualization. The results demonstrate that scJoint outperforms existing methods in computational efficiency and cell-type label accuracy, providing a more comprehensive understanding of cellular phenotypes.
NATURE BIOTECHNOLOGY
(2022)
Review
Multidisciplinary Sciences
Ton N. Schumacher et al.
Summary: This article discusses the current knowledge on TLSs in cancer, focusing on the drivers of TLS formation, the function and contribution of TLSs to the antitumor immune response, and the potential of TLSs as therapeutic targets in human cancers.
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Biochemical Research Methods
Kai Cao et al.
Summary: Motivated by the need for effective approaches to integrate single-cell multi-omics data, this study presents Pamona, a partial Gromov-Wasserstein distance-based manifold alignment framework. It aims to delineate and represent the shared and dataset-specific cellular structures across modalities. Pamona demonstrates superior performance in accurately identifying shared and dataset-specific cells, recovering and aligning cellular structures, outperforming existing methods. The framework also allows for the incorporation of prior information to enhance alignment quality.
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Biotechnology & Applied Microbiology
Mohammad Lotfollahi et al.
Summary: scArches is a deep learning strategy for mapping query datasets on top of a reference, allowing efficient and decentralized reference construction while preserving biological state information and removing batch effects. It generalizes to multimodal reference mapping and can impute missing modalities.
NATURE BIOTECHNOLOGY
(2022)
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Biotechnology & Applied Microbiology
Zhi-Jie Cao et al.
Summary: GLUE is a computational framework that bridges the gap between different omics layers by modeling regulatory interactions, and it outperforms state-of-the-art tools in accuracy, robustness, and scalability for heterogeneous single-cell multi-omics data. It has been successfully applied in various challenging tasks.
NATURE BIOTECHNOLOGY
(2022)
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Biochemical Research Methods
Bin Li et al.
Summary: This study compares 16 integration methods using 45 paired datasets and 32 simulated datasets, finding that Tangram, gimVI, and SpaGE perform best in predicting RNA transcript distribution, while Cell2location, SpatialDWLS, and RCTD are top-performing methods for cell type deconvolution. The benchmark pipeline provided in the study helps researchers in selecting optimal integration methods for their datasets. The comprehensive benchmarking analysis presented in this work evaluates computational methods integrating spatial and single-cell transcriptomics data for transcript distribution prediction and cell type deconvolution.
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Multidisciplinary Sciences
Lei Xiong et al.
Summary: SCALEX is a deep-learning method for online integration of diverse single-cell data. It accurately aligns different modalities of single-cell data, retains true biological differences, and has superior performance in large-scale single-cell applications.
NATURE COMMUNICATIONS
(2022)
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Computer Science, Artificial Intelligence
Xiaoyang Chen et al.
Summary: The authors propose a probabilistic generative model called EpiAnno to automatically annotate single-cell chromatin accessibility sequencing (scCAS) data. The model is validated on multiple datasets and demonstrates advantages in interpretable embedding and biological implications.
NATURE MACHINE INTELLIGENCE
(2022)
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Biochemical Research Methods
Pinar Demetci et al.
Summary: Single-cell alignment using optimal transport (SCOT) is an unsupervised algorithm that aligns single-cell multiomics data by constructing k-nearest neighbor (k-NN) graphs and using coupling matrices.
JOURNAL OF COMPUTATIONAL BIOLOGY
(2022)
Article
Multidisciplinary Sciences
Karren Dai Yang et al.
Summary: The authors use autoencoders to learn a probabilistic coupling and map different data modalities to a shared latent space, presenting an approach for integrating vastly different modalities. The integration of imaging and transcriptomics is still an open challenge, but this method provides a framework for diverse applications in biomedical discovery.
NATURE COMMUNICATIONS
(2021)
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Cell Biology
Camille Philippot et al.
Summary: Thalamic astrocytes and oligodendrocytes form panglial networks via gap junctions, playing key roles in providing energy substrates for sustaining neuronal activity. Loading astrocytes or oligodendrocytes with glucose or lactate can rescue the decline of stimulation-induced field post-synaptic potential amplitudes during extracellular glucose deprivation, with monocarboxylate and glucose transporters being required for this activity. Oligodendrocytes mainly assist astrocytes in metabolite transfer to the postsynapse, as demonstrated in mice deficient in astrocyte or oligodendroglial coupling.
Article
Biochemical Research Methods
Tim Stuart et al.
Summary: Signac is a comprehensive toolkit for the analysis of single-cell chromatin data, enabling end-to-end analysis and interoperability with the Seurat package for multimodal analysis.
Article
Biochemical Research Methods
Tommaso Biancalani et al.
Summary: Tangram is a versatile tool that aligns single-cell and single-nucleus RNA-seq data to spatially resolved transcriptomics data using deep learning. By addressing the loss of spatial information in single-cell data and overcoming technical limitations, this method demonstrates genome-wide anatomically integrated spatial mapping at single-cell resolution on healthy mouse brain tissue.
Article
Biochemical Research Methods
Adam Gayoso et al.
Summary: totalVI is a framework for end-to-end joint analysis of CITE-seq data which probabilistically represents data as a composite of biological and technical factors, providing a cohesive solution for common analysis tasks. It demonstrates strong performance in tasks such as dimensionality reduction, dataset integration, correlation estimation, and differential expression testing.
Article
Biochemistry & Molecular Biology
Marc Elosua-Bayes et al.
Summary: SPOTlight is a computational tool that integrates spatial transcriptomics with single-cell RNA sequencing data to infer the location of cell types and states within complex tissues. Its high prediction accuracy and flexible application spectrum were demonstrated through applications in mouse brain and human pancreatic cancer.
NUCLEIC ACIDS RESEARCH
(2021)
Article
Multidisciplinary Sciences
Rongxin Fang et al.
Summary: The paper introduces SnapATAC, a software package for analyzing single cell ATAC-seq datasets, which can dissect cellular heterogeneity and map cellular states' trajectories. The Nystrom method allows processing data from up to a million cells, and it incorporates existing tools for single cell ATAC-seq dataset analysis. SnapATAC is applied to mouse secondary motor cortex profiles and identifies candidate regulatory elements and cell-type specific transcriptional regulators.
NATURE COMMUNICATIONS
(2021)
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Multidisciplinary Sciences
Alma Andersson et al.
Summary: This study utilized Spatial Transcriptomics technology to investigate gene expression in HER2-positive breast tumors, identifying shared gene signatures for immune and tumor processes and developing a predictive model for cellular interactions.
NATURE COMMUNICATIONS
(2021)
Review
Cell Biology
Shaoshan Mai et al.
Summary: Platelets have been identified as crucial players in hemostasis, thrombosis, and cancer progression. Research has shown that platelets can promote tumorigenesis and metastasis by interacting with cancer cells in various ways. Understanding the interaction between pancreatic cancer and platelets, as well as the underlying mechanisms, may offer insights into potential diagnostic and therapeutic strategies for combating the devastating disease.
FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY
(2021)
Review
Biotechnology & Applied Microbiology
Ricard Argelaguet et al.
Summary: The development of single-cell multimodal assays has provided a powerful tool for investigating cellular heterogeneity in multiple dimensions. Data integration is a key challenge in analyzing single-cell multimodal data, with existing strategies utilizing similar mathematical ideas but having distinct goals and principles.
NATURE BIOTECHNOLOGY
(2021)
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Biochemical Research Methods
Chunman Zuo et al.
Summary: DCCA is a computational tool for joint analysis of single-cell multi-omics data, capable of dissecting cellular heterogeneity, denoising and aggregating data, and constructing links between multi-omics data. By fine-tuning networks and inferring new transcriptional regulatory relations, DCCA demonstrates superior capability in analyzing and understanding complex biological processes.
Article
Biochemical Research Methods
Chen Shengquan et al.
Summary: This study introduces a reference-based method called stPlus, which leverages scRNA-seq data to enhance spatial transcriptomics research. stPlus outperforms baseline methods in terms of gene and cell-level Spearman correlation coefficients, and its performance can be systematically evaluated through a clustering-based approach.
Article
Biochemistry & Molecular Biology
Yuhan Hao et al.
Summary: The study introduces a weighted-nearest neighbor analysis framework to learn the relative utility of each data type in each cell, enabling integrative analysis of multiple modalities. Applied to a CITE-seq dataset, the method constructs a multimodal reference atlas of the circulating immune system and successfully identifies and validates previously unreported lymphoid subpopulations.
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Genetics & Heredity
Sunny Z. Wu et al.
Summary: This study presents a comprehensive transcriptional atlas of the cellular architecture of breast cancer, identifying recurrent neoplastic cell heterogeneity and new immune cell populations associated with clinical outcomes. A multi-omic atlas integrates single-cell RNA sequencing, spatial transcriptomics, and immunophenotyping to stratify breast cancer into nine ecotypes with unique cellular compositions and clinical outcomes.
Review
Genetics & Heredity
Sophia K. Longo et al.
Summary: Integrating single-cell RNA sequencing with spatial transcriptomics allows for the localization of transcriptionally characterized single cells within their native tissue context. While scRNA-seq identifies cell subpopulations within tissue, it does not capture their spatial distribution or reveal local networks of intercellular communication. Recent techniques like multiplexed in situ hybridization and in situ sequencing, defined as high-plex RNA imaging, can help address these limitations. However, the need for approaches to integrate single-cell and spatial data remains, given that no current method provides as complete a scope of the transcriptome as scRNA-seq.
NATURE REVIEWS GENETICS
(2021)
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Biotechnology & Applied Microbiology
Mika Sarkin Jain et al.
Summary: MultiMAP is a novel algorithm for dimensionality reduction and integration of multimodal data, which is particularly suitable for single-cell biology. It outperforms current approaches in analyzing single-cell transcriptomics, chromatin accessibility, methylation, spatial data, etc. The application of MultiMAP enables quantitative comparison of transcription factor expression and binding site accessibility over the course of T cell differentiation.
Article
Biotechnology & Applied Microbiology
Boying Gong et al.
Summary: Cobolt is a novel method designed for analyzing data from joint-modality platforms and integrating multiple datasets across different modalities. It demonstrates its integration capabilities by jointly analyzing multi-modality data of gene expression and chromatin accessibility with single-cell RNA-seq and ATAC-seq datasets.
Article
Biotechnology & Applied Microbiology
Lihua Zhang et al.
Summary: A new integration method called scMC has been proposed by researchers to remove technical variation while preserving biological variation when integrating and comparing single-cell genomics datasets across different experiments.
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Reuben Moncada et al.
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