98%
921
2 minutes
20
Embryonic development is a fundamental physiological process that can provide tremendous insights into stem cell biology and regenerative medicine. In this process, cell fate decision is highly heterogeneous and dynamic, and investigations at the single-cell level can greatly facilitate the understanding of the molecular roadmap of embryonic development. Rapid advances in the technology of single-cell sequencing offer a perfectly useful tool to fulfill this purpose. Despite its great promise, single-cell sequencing is highly interdisciplinary, and successful applications in specific biological contexts require a general understanding of its diversity as well as the advantage versus limitations for each of its variants. Here, the technological principles of single-cell sequencing are consolidated and its applications in the study of embryonic development are summarized. First, the technology basics are presented and the available tools for each step including cell isolation, library construction, sequencing, and data analysis are discussed. Then, the works that employed single-cell sequencing are reviewed to investigate the specific processes of embryonic development, including preimplantation, peri-implantation, gastrulation, and organogenesis. Further, insights are provided on existing challenges and future research directions.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1002/adbi.202101151 | DOI Listing |
Sci Adv
September 2025
Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here, we performed single-nucleus multiomics and spatial transcriptomics in up to 32 nondiabetic (ND), autoantibody-positive (AAB), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine subtypes.
View Article and Find Full Text PDFSci Adv
September 2025
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
Breastfeeding is essential for reducing infant morbidity and mortality, yet exclusive breastfeeding rates remain low, often because of insufficient milk production. The molecular causes of low milk production are not well understood. Fresh milk samples from 30 lactating individuals, classified by milk production levels across postpartum stages, were analyzed using genomic and microbiome techniques.
View Article and Find Full Text PDFSci Adv
September 2025
Department of Cell & Molecular Biology, St. Jude Children's Research Hospital, Memphis, TN, USA.
Somatic mitochondrial DNA (mtDNA) mutations are frequently observed in tumors, yet their role in pediatric cancers remains poorly understood. The heteroplasmic nature of mtDNA-where mutant and wild-type mtDNA coexist-complicates efforts to define its contribution to disease progression. In this study, bulk whole-genome sequencing of 637 matched tumor-normal samples from the Pediatric Cancer Genome Project revealed an enrichment of functionally impactful mtDNA variants in specific pediatric leukemia subtypes.
View Article and Find Full Text PDFSci Transl Med
September 2025
Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Precision Cancer Medicine Center, Fudan University Shanghai Cancer Center, Shanghai 200032, P. R. China.
Triple-negative breast cancers (TNBCs) lack predictive biomarkers to guide immunotherapy, especially during early-stage disease. To address this issue, we used single-cell RNA sequencing, bulk transcriptomics, and pathology assays on samples from 171 patients with early-stage TNBC receiving chemotherapy with or without immunotherapy. Our investigation identified an enriched subset of interferon (IFN)-induced CD8 T cells in early TNBC samples, which predict immunotherapy nonresponsiveness.
View Article and Find Full Text PDFIEEE Trans Comput Biol Bioinform
September 2025
The rapid advancement of single-cell sequencing technology has generated vast amounts of multi-omics data, presenting unprecedented opportunities for single-cell multi-omics clustering analysis. However, existing single-cell clustering algorithms focus on extracting shared representations, overlooking the interactions and correlations among cells. This oversight inevitably leads to biased or confounded cell clustering results.
View Article and Find Full Text PDF