PaperR-(3)-2022-09

我们在这个 Paper Reading 系列分享一些和生物医学相关的文献。

以下是我们九月份看到的一些比较有意思的文章：

生物医学研究

Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations

Highlights: "We present a method to automatically identify and track nuclei in time-lapse microscopy recordings of entire developing embryos. The method combines deep learning and global optimization. On a mouse dataset, it reconstructs 75.8% of cell lineages spanning 1 h, as compared to 31.8% for the competing method. Our approach improves understanding of where and when cell fate decisions are made in developing embryos, tissues, and organs."

Link: https://www.nature.com/articles/s41587-022-01427-7

单细胞测序

Control of cell state transitions

Highlights: "Understanding cell state transitions and purposefully controlling them is a longstanding challenge in biology. Here we present cell state transition assessment and regulation (cSTAR), an approach for mapping cell states, modelling transitions between them and predicting targeted interventions to convert cell fate decisions. By integrating signalling and phenotypic data, cSTAR models how cells manoeuvre in Waddington’s landscape and make decisions about which cell fate to adopt. Testing cSTAR in a cellular model of differentiation and proliferation shows a high correlation between quantitative predictions and experimental data."

Link: https://www.nature.com/articles/s41586-022-05194-y

Pyro-Velocity: Probabilistic RNA Velocity inference from single-cell data

Highlights: "Single-cell RNA Velocity has dramatically advanced our ability to model cellular differentiation and cell fate decisions. However, current preprocessing choices and model assumptions often lead to errors in assigning developmental trajectories. Here, we develop, Pyro-Velocity, a Bayesian, generative, and multivariate RNA Velocity model to estimate the uncertainty of cell future states. This approach models raw sequencing counts with the synchronized cell time across all expressed genes to provide quantifiable and improved information on cell fate choices and developmental trajectory dynamics. Pyro-Velocity is a fully generative Bayesian method with uncertainty estimation of velocity vector fields and shared latent time, based on raw counts and without ad-hoc preprocessing steps."

Link: https://www.biorxiv.org/content/10.1101/2022.09.12.507691v2.full

空间转录组

Cell type-specific inference of differential expression in spatial transcriptomics

Highlights: "A central problem in spatial transcriptomics is detecting differentially expressed (DE) genes within cell types across tissue context. We present C-SIDE, that identifies cell type-specific DE in spatial transcriptomics, accounting for localization of other cell types. We model gene expression as an additive mixture across cell types of log-linear cell type-specific expression functions. C-SIDE’s framework applies to many contexts: DE due to pathology, anatomical regions, cell-to-cell interactions and cellular microenvironment. Furthermore, C-SIDE enables statistical inference across multiple/replicates."

Link: https://www.nature.com/articles/s41592-022-01575-3

SiGra: Single-cell spatial elucidation through image-augmented graph transformer

Highlights: "In this work, we developed a novel method, Single-cell spatial elucidation through image-augmented Graph transformer (SiGra), to reveal spatial domains and enhance the substantially sparse and noisy transcriptomics data. SiGra applies hybrid graph transformers over a spatial graph that comprises high-content images (e.g., from NanoString, CosMx platforms) and gene expressions of individual cells. SiGra improves the characterization of intratumor heterogeneity and intercellular communications in human lung cancer samples, meanwhile recovers the known microscopic anatomy in both human brain and mouse liver tissues."

Link: https://www.biorxiv.org/content/10.1101/2022.08.18.504464v2.full

Decomposing spatial heterogeneity of cell trajectories with Paella

Highlights: "Spatial transcriptomics provides a unique opportunity to study continuous biological processes in a spatial context. We developed Paella, a computational method to decompose a cell trajectory into multiple spatial sub-trajectories and identify genes with differential temporal patterns across spatial sub-trajectories. Applied to spatial transcriptomics datasets of cancer, Paella identified spatially varying genes associated with tumor progression, providing insights into the spatial heterogeneity of cancer development."

Link: https://www.biorxiv.org/content/10.1101/2022.09.05.506682v1.full

三代测序

SVision: a deep learning approach to resolve complex structural variants

Highlights: "We developed SVision, a deep-learning-based multi-object-recognition framework, to automatically detect and haracterize CSVs from long-read sequencing data. It introduces a sequence-to-image coding schema, adapting variant detection to a problem that is amenable to deep-learning frameworks."

Link: https://www.nature.com/articles/s41592-022-01609-w

序列分析

Scalable, ultra-fast, and low-memory construction of compacted de Bruijn graphs with Cuttlefish 2

Highlights: "The de Bruijn graph is a key data structure in modern computational genomics, and construction of its compacted variant resides upstream of many genomic analyses. As the quantity of genomic data grows rapidly, this often forms a computational bottleneck. We present Cuttlefish 2, significantly advancing the state-of-the-art for this problem. On a commodity server, it reduces the graph construction time for 661K bacterial genomes, of size 2.58Tbp, from 4.5 days to 17–23 h."

Link: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02743-6

GBZ File Format for Pangenome Graphs

Highlights: "Pangenome graphs representing aligned genome assemblies are being shared in the text-based Graphical Fragment Assembly format. As the number of assemblies grows, there is a need for a file format that can store the highly repetitive data space-efficiently. We propose the GBZ file format based on data structures used in the Giraffe short read aligner. The format provides good compression, and the files can be efficiently loaded into in-memory data structures."

Link: https://www.biorxiv.org/content/10.1101/2022.07.12.499787v2.full