We introduce our monthly single cell Literature Blitz column. What new achievements in single cell sequencing has February brought us?
Our February highlights brought us new advances and applications of single cell sequencing in understanding Minimal Residue Disease (MRD) in leukaemia, high-throughput metabolomics, bioenergetics, mapping of brain vasculature, and profiling of cell-state transitions.
Zhang, Y. et al, Elucidating minimal residual disease of paediatric B-cell acute lymphoblastic leukaemia by single-cell analysis, Nature Cell Biology, 2022
Minimal residual disease that persists after chemotherapy is the most valuable prognostic marker for haematological malignancies and solid cancer, which has already been used to guide therapy intensity and justify bone-marrow (BM) transplantation. Unfortunately, our understanding of the resistance elicited in minimal residual disease is limited due to the rarity and heterogeneity of the residual cells.
To overcome this limitation, Zhang et al performed single cell RNA and single cell BCR sequencing on paediatric and adult BM B cells, effectively defining the B cell differentiation map and training classifiers for deep learning in B cell acute lymphoblastic leukemia (B-ALL). This allowed for comparison of whole transcriptomes and immune repertoire characteristics between B-cells of different stages (diagnosis, residual, and relapse).
The results revealed the upregulation of reactive oxygen species and MYC related pathways, as well as the G2-M components in relapse patients. Both in vitro and in vivo models demonstrated that inhibition of the hypoxia pathway sensitized leukaemic cells to chemotherapy, showing the power of single cell sequencing in identifying therapeutic targets in resistant cancer.
Figure1: Overview of the experimental design involving single cell RNA and BCR sequencing of bone marrow samples from patients at diagnosis, MRD or relapse.
Hancock, E. et al, High-throughput detection and quantification of phosphatidylcholines and sphingomyelins from single cells by chip-based nanoelectrospray ionization, bioRxiv preprint, 2022.
Advances in single-cell high-throughput methods has given us insight into the heterogeneity of cells in biological systems. Metabolomics is invaluable for biological research, especially in disease states such as cancer, but has been hindered on the single-cell level by technical issues, including low level of metabolites in the cell, difficulties of amplifying the signal, and the resolution of metabolome measurement techniques.
Hancock et al developed a high-throughput method for detection and quantification of a wide range of phosphatidylcholine (PC) and sphingomyelin (SM) species from single cells that combines fluorescence-assisted cell sorting (FACS) with automated chip-based nanoelectrospray ionisation (nanoESI) and shotgun lipidomics.
Researchers showed that immobilizing cells in wells overcomes one of the most significant limitations of single cell metabolomics. They also showed that prostate cells can be clustered according to their theoretical PC and SM content. Further single cell RNA sequencing can be combined with this high-throughput metabolomics approach for a more holistic overview of cell states.
Figure 2: tSNE plots of phosphatidylcholine (PC) and sphingomyelin (SM) species detected in single isolated A C2C12 and B HepG2cells. Cells were cultured under normal conditions (CON) or after overnight culture with 50 μM docosahexaenoic acid (DHA).
Przystal, JM et al, Imipridones affect tumor bioenergetics and promote cell lineage differentiation in diffuse midline gliomas (DMG), Neuro Oncol. 2022
Pediatric diffuse midline gliomas (DMGs) are incurable childhood cancers. The imipridone ONC201 has shown early clinical efficacy in a subset of DMGs, including primary human in vitro and animal in vivo models. Cell lineage differentiation and drug-altered pathways were defined using bulk and single cell RNA-seq.
Przystal et al showed that ONC201 and ONC206 reduce viability of DMG cells in nM concentrations and extend survival. ClpP activation by both drugs results in impaired tumor cell metabolism, mitochondrial damage, ROS production, activation of integrative stress response and apoptosis. Single cell RNA sequencing revealed that imipridone treatment triggered a lineage shift from a proliferative, oligodendrocyte precursor-like state to a mature, astrocyte-like state. The data provided a foundation for new clinical trials using ONC206 for the treatment of DMGs
Figure 3: Imprimidone ONC201 treatment triggered a lineage shift from a proliferative, oligodendrocyte precursor-like state to a mature, astrocyte-like state.
Yang, A.C., et al. A human brain vascular atlas reveals diverse mediators of Alzheimer’s risk. Nature (2022).
The human brain vasculature is of great importance: its dysfunction causes disability and death, and the specialized structure it forms, the blood brain barrier (BBB), impedes treatment of nearly all brain disorders. So far, there is no molecular map of the human brain vasculature.
Yang, A.C., et al created a vascular cell atlas of the human brain and compared the BBB composition between healthy and Alzheimer’s brains. They developed a method called VINE-seq to isolate nuclei from brain microvessels without damaging them, taken from post-mortem frozen brains. They performed single-nucleus sequencing from 9 individuals with AD and 8 healthy individuals.
The results identified 15 major cell types including both immune and non-immune cells and comparing brain endothelial cells between human and mice revealed species specific genes. They also mapped different types of Pericytes, smooth-muscle cells (SMCs) and fibroblasts, and characterized DEGs in each cell type. Vasculature from AD patients had strong reduction in various cells such as endothelial cells, SMCs, pericytes and fibroblast-like cells
Figure 4: Enrichment of vascular and perivascular cell types from the human cortex and hippocampus.
Qiu X., et al. Mapping transcriptomic vector fields of single cell. Cell, 2022.
Recent developments in single-cell genomics have enabled profiling of cell-state transitions at unprecedented resolution. However, due to their destructive nature, it is generally infeasible to follow the same cell over time. Advances in single-cell profiling have fueled the development of computational approaches for inferring cellular dynamics from snapshot measurements. Chief among them are pseudotime-based methods.
Here, Qiu et al introduce a framework for constructing and interpreting single-cell transcriptomic vector fields. The framework delivers four innovations: (1) reconciling RNA metabolic labeling and intrinsic splicing kinetics, (2) developing a general algorithm for robustly reconstructing the continuous transcriptomic vector field from discrete, sparse, and noisy single-cell measurements, (3) joining the scalability of machine learning-based vector field reconstruction methods with the interpretability of differential geometry analyses, including Jacobian, acceleration, curvature, and divergence, to gain further biological insights, and (4) leveraging the analytical vector field reconstructed directly from scRNA-seq datasets.
Figure 5: Dynamo constructs transcriptomic vector fields from single-cell data and enables predictive modeling of cell-state regulatory mechanisms, perturbation outcomes, and optimal paths for cell state transitions.
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