Five Breakthrough Discoveries Unveiled by Single-cell Transcriptomics

Biological Applications

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      The revolution of single-cell transcriptomics (single-cell RNA-sequencing) has led to numerous groundbreaking discoveries across clinical and research settings, from cancer diagnostics to brain development. The single-cell revolution has also fueled efforts to establish cell atlases for organs across species and developmental timepoints. These atlases are crucial for our understanding of tissues in health and disease. They have also contributed to the discovery of previously unidentified cell types in various organs. In this article, we explore five key breakthroughs enabled by single-cell transcriptomics, showcasing its transformative potential in biomedical research.

1.Discovery of Disease-related Cell Type in Alzheimer’s

      Beyond atlases, thousands of researchers and clinicians worldwide are using scRNA-seq on a smaller scale to identify and characterize function (and dysfunction) of cell types across systems. In a mouse model of Alzheimer’s disease, researchers identified a novel cell type, disease-associated microglia (DAM), that was specifically related to the disease pathology. Microglia are considered the immune cells of the brain and act as macrophages, with the unique ability to digest cellular debris and dead cells. DAM specifically responds to neurodegeneration and plays a protective role, therefore representing potential targets for novel therapies. Researchers were also able to characterize cellular pathways and genes involved in DAM activation.

2. Identification of Novel Glioma Target

      A deep understanding of the molecular signatures and pathways in tumor cells is crucial for the identification of potential drug targets. In a study on glioma, a brain tumor, researchers conducted scRNA-seq on isolated cells from patient tumor samples and reported various significant gene expression patterns and altered pathways. One of these genes was found to be involved in the immune response and its deletion in cancer cells was subsequently found to improve survival. This demonstrates the utility of scRNA-seq studies for the identification of novel treatment targets in cancer.

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3. Characterizing Pathways Involved in Epilepsy

      Many individuals with epilepsy do not respond to treatment – a case referred to as refractory epilepsy. In a recent study, investigators sought to characterize molecular pathways and cellular phenotypes in resected patient tissue. They identified a pro-inflammatory microenvironment that was further dissected by combining scRNA-seq data with CITE-seq data that allows for the identification of surface markers of immune cells. By doing so, Kumar et al. were able to map the specific immune cell types that may be contributing to pathology.

Diving into human brain epileptic lesions using scRNA-seq and CITE-seq to combine gene and surface protein expression at single-cell resolution. Kumar et al., Nature Neuroscience volume 25, pages 956–966 (2022).

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4. The Allen Brain Project

      The Allen Brain Project is a massive undertaking that aimed to profile every cell type in the mouse brain, which is widely used as a research model and in pre-clinical trials. The long-awaited results were published in a collection of papers in the December 2023 issue of Nature. Researchers collected cells from brains of over 300 mice to compile the huge dataset. Using single-cell RNA-sequencing (scRNA-seq), researchers mapped transcriptomes of 4 million cells, leading to the identification of 5,000 cell types in the mouse brain. This breakthrough will facilitate mouse brain studies as it represents a reference atlas to which any new transcriptome datasets can be compared. This will be particularly valuable for disease model comparisons and drug discovery studies as cell-specific effects can be characterized across different cell types that may have been unknown previously. Furthermore, this atlas highlights the extensive cellular heterogeneity of the mouse brain, which makes the prospect of atlasing the far more complex human brain very exciting. While there have been a few human atlas studies (one of which is discussed below), a much larger number of cells (far more than 4M) will be needed to provide a complete atlas of the human brain. The full dataset is publicly available here.

Whole mouse brain atlas UMAP representation of all cell types coloured by class (b) and subclass (c). Yao et al. Nature volume 624, pages 317–332 (2023).


5. Human Cell Atlas

      The Human Cell Atlas is another huge initiative that was established by the Tabula Sapiens Consortium to map single-cell transcriptomes across 24 tissues and organs. These include the bladder, blood, bone marrow, eye, fat, heart, kidney, large intestine, liver, lung, lymph node, mammary, muscle, pancreas, prostate, salivary gland, skin, small intestine, spleen, thymus, tongue, trachea, uterus, and vasculature. An initial study was published in Science in 2022 and the dataset consisted of a total of 500K cells. Samples were collected from organ donors. Importantly, some findings may be related to the aging process as many of the donors were of older age (mean of 51 years). More samples from younger donors can make the data more representative of cell types in the healthy context. As with other atlases, this atlas – which is still being built – is highly valuable as a reference dataset to which studies on different human tissues in health, disease, and across the lifetime can be compared. The atlas is continuously being expanded and has many valuable implications. For instance, a deep understanding of the molecular signatures of specific cell types allows us to identify changes in the diagnostic setting. The identification of cell type-specific markers and transcription factors also facilitates the process of engineering virtually any cell type from stem cells.

      There are hundreds of similar examples in which scRNA-seq datasets contain puzzle pieces that are waiting to be uncovered. Get in touch with us to discuss your mystery and let us tell you how single-cell transcriptomics could reveal the answers!

      As more and more reference datasets are generated, scRNA-seq studies become much simpler as there is no longer a need to build a ‘control’ dataset and researchers can instead focus on their specific disease, treatment, or cell type of interest. Scipio bioscience’s cloud-based and fully automated analysis software, Cytonaut, can integrate the data generated with the Asteria kit to publicly available datasets to provide precise comparisons and highlight biologically relevant findings.

Blog article authored by Dr. Joy Ismail, PhD, for Scipio bioscience and originally published on the Scipio bioscience website on 26/06/2024. All rights reserved.