Deciphering the cell dynamics in complex biological systems is of great significance for understanding the mechanisms of life and facilitating disease treatment. Recent advances in single-cell sequencing technologies have enabled the measurement of single-cell characteristics over multiple time points. However, the integration and analysis of these dynamic single-cell data face many challenges and raise new demands for computational methodologies. In this review, we first elaborate these challenges in the context of experimental limitations, data features, and biological discoveries. Then, we provide an overview of the algorithmic advancements across four key tasks: inferring single-cell dynamics, dissecting dynamic mechanisms, predicting future cell fates, and integrating lineage tracing information to characterize cell dynamics. Finally, we discuss that the cutting-edge developments in biological technologies and artificial intelligence algorithms may greatly enhance our ability to explore complex life processes from a spatiotemporal systemic perspective.

Deciphering cell-type-specific gene regulatory networks (ctGRNs) is crucial for elucidating fundamental biological processes, such as tissue development and cancer progression. However, accurately inferring ctGRNs from high-dimensional transcriptomic data poses a significant challenge, primarily due to issues like data sparsity, cell heterogeneity, and over-smoothing (i.e. the tendency of node features to become indistinguishable after many graph convolution layers) in deep learning models. To tackle these obstacles, we present GeneLink+, an innovative framework for ctGRN inference leveraging directed graph link prediction (i.e. inferring causal regulator-target edges) tasks. Building upon the robust predictive capabilities of its primary version, GENELink, GeneLink+ incorporates residual-GATv2 blocks, which synergize dynamic attention mechanisms with residual connections. This architecture effectively mitigates information loss during the aggregation process and preserves cell-type-specific gene features, thereby enhancing the identification of regulatory mechanisms as well as the model's interpretability. Furthermore, GeneLink+ uses a modified dot product scheme with learnable weight parameters to adaptively prioritize informative gene pairs when scoring regulatory relationships, thus enabling more precise causal edge attribution. Comprehensive benchmarking across seven datasets demonstrated that GeneLink+ either outperforms or matches the performance of existing state-of-the-art methods in terms of predictive accuracy and biological relevance. Additionally, applications to a wide array of transcriptomic data, encompassing single-cell ribonucleic acid sequencing, small nuclear ribonucleic acid sequencing, and spatially resolved transcriptomics, have unveiled pivotal causal regulatory relationships in blood immune cells, Alzheimer's disease, and breast cancer.

SUMMARY: Single-cell RNA-seq (scRNA-seq) is a powerful technique for decoding the complex cellular compositions in the tumor microenvironment (TME). As previous studies have defined many meaningful cell subtypes in several tumor types, there is a great need to computationally transfer these labels to new datasets. Also, different studies used different approaches or criteria to define the cell subtypes for the same major cell lineages. The relationships between the cell subtypes defined in different studies should be carefully evaluated. In this updated package scCancer2, designed for integrative tumor scRNA-seq data analysis, we developed a supervised machine learning framework to annotate TME cells with annotated cell subtypes from 15 scRNA-seq datasets with 594 samples in total. Based on the trained classifiers, we quantitatively constructed the similarity maps between the cell subtypes defined in different references by testing on all the 15 datasets. Secondly, to improve the identification of malignant cells, we designed a classifier by integrating large-scale pan-cancer TCGA bulk gene expression datasets and scRNA-seq datasets (10 cancer types, 175 samples, 663 857 cells). This classifier shows robust performances when no internal confidential reference cells are available. Thirdly, scCancer2 integrated a module to process the spatial transcriptomic data and analyze the spatial features of TME. AVAILABILITY AND IMPLEMENTATION: The package and user documentation are available at http://lifeome.net/software/sccancer2/ and https://doi.org/10.5281/zenodo.10477296.

Cell-cell communication events (CEs) are mediated by multiple ligand-receptor (LR) pairs. Usually only a particular subset of CEs directly works for a specific downstream response in a particular microenvironment. We name them as functional communication events (FCEs) of the target responses. Decoding FCE-target gene relations is: important for understanding the mechanisms of many biological processes, but has been intractable due to the mixing of multiple factors and the lack of direct observations. We developed a method HoloNet for decoding FCEs using spatial transcriptomic data by integrating LR pairs, cell-type spatial distribution and downstream gene expression into a deep learning model. We modeled CEs as a multi-view network, developed an attention-based graph learning method to train the model for generating target gene expression with the CE networks, and decoded the FCEs for specific downstream genes by interpreting trained models. We applied HoloNet on three Visium datasets of breast cancer and liver cancer. The results detangled the multiple factors of FCEs by revealing how LR signals and cell types affect specific biological processes, and specified FCE-induced effects in each single cell. We conducted simulation experiments and showed that HoloNet is more reliable on LR prioritization in comparison with existing methods. HoloNet is a powerful tool to illustrate cell-cell communication landscapes and reveal vital FCEs that shape cellular phenotypes. HoloNet is available as a Python package at https://github.com/lhc17/HoloNet.

The inference of gene co-expression associations is one of the fundamental tasks for large-scale transcriptomic data analysis. Due to the high dimensionality and high noises in transcriptomic data, it is difficult to infer stable gene co-expression associations from single dataset. Meta-analysis of multisource data can effectively tackle this problem. We proposed Joint Embedding of multiple BIpartite Networks (JEBIN) to learn the low-dimensional consensus representation for genes by integrating multiple expression datasets. JEBIN infers gene co-expression associations in a nonlinear and global similarity manner and can integrate datasets with different distributions in linear time complexity with the gene and total sample size. The effectiveness and scalability of JEBIN were verified by simulation experiments, and its superiority over the commonly used integration methods was proved by three indexes on real biological datasets. Then, JEBIN was applied to study the gene co-expression patterns of hepatocellular carcinoma (HCC) based on multiple expression datasets of HCC and adjacent normal tissues, and further on latest HCC single-cell RNA-seq data. Results show that gene co-expressions are highly different between bulk and single-cell datasets. Finally, many differentially co-expressed ligand-receptor pairs were discovered by comparing HCC with adjacent normal data, providing candidate HCC targets for abnormal cell-cell communications.

Molecular heterogeneities and complex microenvironments bring great challenges for cancer diagnosis and treatment. Recent advances in single-cell RNA-sequencing (scRNA-seq) technology make it possible to study cancer cell heterogeneities and microenvironments at single-cell transcriptomic level. Here, we develop an R package named scCancer, which focuses on processing and analyzing scRNA-seq data for cancer research. Except basic data processing steps, this package takes several special considerations for cancer-specific features. Firstly, the package introduced comprehensive quality control metrics. Secondly, it used a data-driven machine learning algorithm to accurately identify major cancer microenvironment cell populations. Thirdly, it estimated a malignancy score to classify malignant (cancerous) and non-malignant cells. Then, it analyzed intra-tumor heterogeneities by key cellular phenotypes (such as cell cycle and stemness), gene signatures and cell-cell interactions. Besides, it provided multi-sample data integration analysis with different batch-effect correction strategies. Finally, user-friendly graphic reports were generated for all the analyses. By testing on 56 samples with 433 405 cells in total, we demonstrated its good performance. The package is available at: http://lifeome.net/software/sccancer/.