Single-Cell Genomics in Disease Research and Diagnostics

Advances in single-cell genomics are resolving cell-type-specific features of pathological conditions

Dmitry Velmeshev, PhD

Human diseases often affect specific cell types within organ systems. In some disorders, such as amyloid lateral sclerosis (ALS), the disease impacts a single cell type representing a minor population of all cells in the tissue, as is the case for the motor neurons in ALS. Because tissues are composed of heterogeneous cell types, studying cell-type-specific features of human pathological conditions demands alternatives to conventional genomics approaches, such as bulk tissue RNA sequencing.

Recent advances in single-cell genomics, in particular single-cell RNA sequencing, are opening new avenues for identifying the exact molecular changes that are associated with pathology in specific cell types. New research utilizing single-cell genomics to better understand human disease conditions is already providing novel insights and promises to help identify highly specific and diagnostic biomarkers and therapeutic targets.

Dissecting roles of immune cells in cancer and tumor heterogeneity

The immune system was one of the first systems to be analyzed in detail using single-cell genomics techniques.1 The availability of immune cells, which can be readily purified from the blood, along with historically well-established panels of markers of immune cell types, served to simplify the analysis of single-cell genomics data. Results of these efforts to apply single-cell RNA sequencing to human immune cells led to characterization of novel subtypes of lymphoid2 and myeloid3 cells as well as dynamic molecular events underlying differentiation of megakaryocyte and myeloid progenitors.

Importantly, single-cell transcriptomics can be used not only to characterize unbiased subtypes of immune cells and their differentiation but also to obtain the repertoire of T and B cell clones mediating adaptive immune response. To this end, researchers recently developed a computational method to utilize single-cell RNA-seq data to reconstruct full-length sequences of T cell receptors (TCR). Combined single-cell gene expression and TCR analysis has since been applied to profile T cell repertoires in liver carcinoma4 and to document changes in T cell clonal composition after checkpoint inhibitor therapy in carcinoma patients. In these ways, single-cell genomics is currently being applied to measure the detailed cellular characteristics of the immune response to tumor growth in cancer patients.

Clinical Lab Manager Cancer Testing Survey  

Besides profiling populations of immune cells infiltrating the tumor, single-cell genomics approaches recently helped to tackle one of the biggest challenges of cancer biology: tumor heterogeneity. It has been increasingly recognized that tumors consist of a number of interacting cell types, derived both from tumor-initiating cells and infiltrating immune and endothelial cells. Tumor heterogeneity is thought to underlie mechanisms of tumor drug resistance. Single-cell transcriptomics studies of various cancer types such as glioblastoma, metastatic melanoma, and myeloid leukemia have identified novel potential drug targets expressed in specific tumor cell types (e.g., cancer stem cells) and genes that may be responsible for drug resistance.

Currently, single-cell approaches to studying cancer have been moving closer to the bedside. For instance, single-cell RNA sequencing has been used to analyze single circulating tumor cells (CTCs), the cells that are shed by the tumors into the patient’s bloodstream. Profiling of CTCs in melanoma and pancreatic cancer holds promise of early diagnosis and dynamic monitoring of cancer recurrence,5 which could dramatically reduce patient mortality in the future.

Analyzing disorders of the human brain

While single-cell analysis of the immune system and tumors is made possible by the accessibility of the patient tissue, which can be obtained through a blood draw, biopsy, or surgical resection, these options are not available when it comes to brain disorders. For most neurological diseases, except for epilepsy, the only time to access the patient’s brain tissue is postmortem. Until recently, single-cell genomics tools required a suspension of live cells to work. However, a novel technique termed single-nucleus RNA sequencing (snRNA-seq) can analyze the RNA of a single cell nuclei isolated from frozen postmortem human brain tissue.6 Nuclear gene expression profiles have been shown to accurately match whole-cell transcriptional profiles, and snRNA-seq has recently been used to identify cell-type-specific gene expression changes in a number of psychiatric and neurological diseases.

In one study, the technique was applied to profile cortical brain tissue of patients with autism spectrum disorder (ASD) and compare the profiles of neuronal and glial subtypes in ASD with those from donors without any brain disorder.7 The authors observed that ASD-associated pathological changes converge on specific cell types, such as the projection neurons in the upper layers of the cortex that are responsible for information flow between cortical regions in the brain. In another study, snRNA-seq was applied to examine the prefrontal cortex of Alzheimer’s disease patients with varying degrees of disease progression. 8 The authors were able to dissect changes in specific cell types that underlie progression of Alzheimer’s pathology, highlighting changes in excitatory neurons and oligodendrocytes related to regulation of myelination. Another group utilized snRNA-seq to profile cell-type-specific changes in the brain white matter of patients with multiple sclerosis (MS), observing changes in subpopulations of oligodendrocytes in MS patients.9 Overall, an increasing number of studies of human brain disease are successfully adopting single-cell genomics to investigate how pathology affects specific brain cell types.

The dawn of personalized diagnostics

Clinical Lab Manager Cancer Testing Survey

Single-cell genomics techniques offer unprecedented resolution of cell types affected by pathological disease conditions. Multi-institutional projects, such as the BRAIN Initiative and Human Cell Atlas, aim to construct a census of cell types of the human brain and the body. The idea is to catalog all of the cell types in the human body and identify sets of marker genes that can be used to distinguish them. At the same time, studies of human pathology on the single-cell level aim to identify genes and pathways that are dysregulated in specific cell types in human disease. Building upon these efforts, it is expected that single-cell analysis of tissue samples from individual patients using techniques like spatial transcriptomics10 will soon become feasible, which will help shift the approach to disease diagnostics and treatments toward personalized medicine.


  1. Stubbington, M. J. T., et al. "Single-cell transcriptomics to explore the immune system in health and disease." Science 358.6359 (2017): 58-63.
  2. Bjorklund, A. K., et al. "The heterogeneity of human Cd127(+) innate lymphoid cells revealed by single-cell RNA sequencing." Nat Immunol 17.4 (2016): 451-60.
  3. Paul, F., et al. "Transcriptional heterogeneity and lineage commitment in myeloid progenitors." Cell 163.7 (2015): 1663-77.
  4. Zheng, C., et al. "Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing." Cell 169.7 (2017): 1342-56.e16.
  5. Yu, M., et al. "RNA sequencing of pancreatic circulating tumour cells implicates WNT signalling in metastasis." Nature 487.7408 (2012): 510-3.
  6. Lake, B. B., et al. "Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain." Science 352.6293 (2016): 1586-90. 
  7. Velmeshev, D., et al. "Single-cell genomics identifies cell type-specific molecular changes in autism." Science 364.6441 (2019): 685-89.
  8. Mathys, H., et al. "Single-cell transcriptomic analysis of Alzheimer's disease." Nature 570.7761 (2019): 332-37. 
  9. Jakel, S., et al. "Altered human oligodendrocyte heterogeneity in multiple sclerosis." Nature 566.7745 (2019): 543-47. 
  10. Burgess, D. J. "Spatial transcriptomics coming of age." Nat Rev Genet 20.6 (2019): 317.