The utilization of ground-breaking large language models (LLMs) accompanied with dialogue system has been progressively prevalent in the medical domain. Nevertheless, the expertise of LLMs in Traditional Chinese Medicine (TCM) remains restricted despite several TCM LLMs proposed recently. Herein, we introduced TCMChat (https://xomics.com.cn/tcmchat), a generative LLM with pre-training (PT) and supervised fine-tuning (SFT) on large-scale curated TCM text knowledge and Chinese Question-Answering (QA) datasets. In detail, we first compiled a customized collection of six scenarios of Chinese medicine as the training set by text mining and manual verification, involving TCM knowledgebase, choice question, reading comprehension, entity extraction, medical case diagnosis, and herb or formula recommendation. Next, we subjected the model to PT and SFT, using the Baichuan2–7B-Chat as the foundation model. The benchmarking datasets and case studies further demonstrate the superior performance of TCMChat in comparison to existing models. Our code, data and model are publicly released on GitHub (https://github.com/ZJUFanLab/TCMChat) and HuggingFace (https://huggingface.co/ZJUFanLab), providing high-quality knowledgebase for the research of TCM modernization with a userfriendly dialogue web tool. [Read More]

Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions. [Read More]

Liver transplantation (LT) is the standard therapy for individuals afflicted with end-stage liver disease. Despite notable advancements in LT technology, the incidence of early allograft dysfunction (EAD) remains a critical concern, exacerbating the current organ shortage and detrimentally affecting the prognosis of recipients. Unfortunately, the perplexing hepatic heterogeneity has impeded characterization of the cellular traits and molecular events that contribute to EAD. Herein, we constructed a pioneering single-cell transcriptomic landscape of human transplanted livers derived from non-EAD and EAD patients, with 12 liver samples collected from 7 donors during the cold perfusion and portal reperfusion stages. Comparison of the 75 231 cells of non-EAD and EAD patients revealed an EAD-associated immune niche comprising mucosal-associated invariant T cells, granzyme B+ (GZMB+) granzyme K+ (GZMK+) natural killer cells, and S100 calcium binding protein A12+ (S100A12+) neutrophils. Moreover, we verified this immune niche and its association with EAD occurrence in two independent cohorts. Our findings elucidate the cellular characteristics of transplanted livers and the EAD-associated pathogenic immune niche at the single-cell level, thus, offering valuable insights into EAD onset. [Read More]

Spatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods. Then we apply SpaTalk to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based spatially resolved transcriptomic data universally, providing valuable insights into spatial inter-cellular tissue dynamics. [Read More]

Advances in single-cell RNA sequencing (scRNA-seq) have furthered the simultaneous classification of thousands of cells in a single assay based on transcriptome profiling. In most analysis protocols, single-cell type annotation relies on marker genes or RNA-seq profiles, resulting in poor extrapolation. Still, the accurate cell-type annotation for single-cell transcriptomic data remains a great challenge. Here, we introduce scDeepSort (https://github.com/ZJUFanLab/scDeepSort), a pre-trained cell-type annotation tool for single-cell transcriptomics that uses a deep learning model with a weighted graph neural network (GNN). Using human and mouse scRNA-seq data resources, we demonstrate the high performance and robustness of scDeepSort in labeling 764 741 cells involving 56 human and 32 mouse tissues. Significantly, scDeepSort outperformed other known methods in annotating 76 external test datasets, reaching an 83.79% accuracy across 265 489 cells in humans and mice. Moreover, we demonstrate the universality of scDeepSort using more challenging datasets and using references from different scRNA-seq technology. Above all, scDeepSort is the first attempt to annotate cell types of scRNA-seq data with a pre-trained GNN model, which can realize the accurate cell-type annotation without additional references, i.e. markers or RNA-seq profiles. [Read More]