Kui Hua

MOTIVATION: Recent developments of spatial transcriptomic sequencing technologies provide powerful tools for understanding cells in the physical context of tissue microenvironments. A fundamental task in spatial gene expression analysis is to identify genes with spatially variable expression patterns, or spatially variable genes (SVgenes). Several computational methods have been developed for this task. Their high computational complexity limited their scalability to the latest and future large-scale spatial expression data. RESULTS: We present SOMDE, an efficient method for identifying SVgenes in large-scale spatial expression data. SOMDE uses self-organizing map to cluster neighboring cells into nodes, and then uses a Gaussian process to fit the node-level spatial gene expression to identify SVgenes. Experiments show that SOMDE is about 5-50 times faster than existing methods with comparable results. The adjustable resolution of SOMDE makes it the only method that can give results in ∼5 min in large datasets of more than 20 000 sequencing sites. SOMDE is available as a python package on PyPI at https://pypi.org/project/somde free for academic use. AVAILABILITY AND IMPLEMENTATION: SOMDE is available for download from PyPI, and the source code is openly available from the Github repository https://github.com/XuegongLab/somde. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Here we use single-cell RNA sequencing to compile a human breast cell atlas assembled from 55 donors that had undergone reduction mammoplasties or risk reduction mastectomies. From more than 800,000 cells we identified 41 cell subclusters across the epithelial, immune and stromal compartments. The contribution of these different clusters varied according to the natural history of the tissue. Age, parity and germline mutations, known to modulate the risk of developing breast cancer, affected the homeostatic cellular state of the breast in different ways. We found that immune cells from BRCA1 or BRCA2 carriers had a distinct gene expression signature indicative of potential immune exhaustion, which was validated by immunohistochemistry. This suggests that immune-escape mechanisms could manifest in non-cancerous tissues very early during tumor initiation. This atlas is a rich resource that can be used to inform novel approaches for early detection and prevention of breast cancer.

Glioblastoma is an incurable brain malignancy. By the time of clinical diagnosis, these tumours exhibit a degree of genetic and cellular heterogeneity that provides few clues to the mechanisms that initiate and drive gliomagenesis1,2. Here, to explore the early steps in gliomagenesis, we utilized conditional gene deletion and lineage tracing in tumour mouse models, coupled with serial magnetic resonance imaging, to initiate and then closely track tumour formation. We isolated labelled and unlabelled cells at multiple stages—before the first visible abnormality, at the time of the first visible lesion, and then through the stages of tumour growth—and subjected cells of each stage to single-cell profiling. We identify a malignant cell state with a neural crest-like gene expression signature that is highly abundant in the early stages, but relatively diminished in the late stage of tumour growth. Genomic analysis based on the presence of copy number alterations suggests that these neural crest-like states exist as part of a heterogeneous clonal hierarchy that evolves with tumour growth. By exploring the injury response in wounded normal mouse brains, we identify cells with a similar signature that emerge following injury and then disappear over time, suggesting that activation of an injury response program occurs during tumorigenesis. Indeed, our experiments reveal a non-malignant injury-like microenvironment that is initiated in the brain following oncogene activation in cerebral precursor cells. Collectively, our findings provide insight into the early stages of glioblastoma, identifying a unique cell state and an injury response program tied to early tumour formation. These findings have implications for glioblastoma therapies and raise new possibilities for early diagnosis and prevention of disease. A study using glioblastoma mouse models, serial magnetic resonance imaging and single-cell profiling details changes in the identity and balance of cellular states from initiation of tumorigenesis to the end point.

SUMMARY: Clustering is a key step in revealing heterogeneities in single-cell data. Most existing single-cell clustering methods output a fixed number of clusters without the hierarchical information. Classical hierarchical clustering (HC) provides dendrograms of cells, but cannot scale to large datasets due to high computational complexity. We present HGC, a fast Hierarchical Graph-based Clustering tool to address both problems. It combines the advantages of graph-based clustering and HC. On the shared nearest-neighbor graph of cells, HGC constructs the hierarchical tree with linear time complexity. Experiments showed that HGC enables multiresolution exploration of the biological hierarchy underlying the data, achieves state-of-the-art accuracy on benchmark data and can scale to large datasets. AVAILABILITY AND IMPLEMENTATION: The R package of HGC is available at https://bioconductor.org/packages/HGC/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.