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Many spatially resolved transcriptomic technologies do not have single-cell resolution but measure the average gene expression for each spot from a mixture of cells of potentially heterogeneous cell types.
Here, we introduce a deconvolution method, conditional autoregressive-based deconvolution (CARD), that combines cell-type-specific expression information from single-cell RNA sequencing (scRNA-seq) with correlation in cell-type composition across tissue locations. Modeling spatial correlation allows us to borrow the cell-type composition information across locations, improving accuracy of deconvolution even with a mismatched scRNA-seq reference.
**CARD** can also impute cell-type compositions and gene expression levels at unmeasured tissue locations to enable the construction of a refined spatial tissue map with a resolution arbitrarily higher than that measured in the original study and can perform deconvolution without an scRNA-seq reference.
Applications to four datasets, including a pancreatic cancer dataset, identified multiple cell types and molecular markers with distinct spatial localization that define the progression, heterogeneity and compartmentalization of pancreatic cancer.
BioTuring
Single-cell RNA sequencing (scRNA-seq) data have allowed us to investigate cellular heterogeneity and the kinetics of a biological process. Some studies need to understand how cells change state, and corresponding genes during the process, but it is challenging to track the cell development in scRNA-seq protocols. Therefore, a variety of statistical and computational methods have been proposed for lineage inference (or pseudotemporal ordering) to reconstruct the states of cells according to the developmental process from the measured snapshot data. Specifically, lineage refers to an ordered transition of cellular states, where individual cells represent points along. pseudotime is a one-dimensional variable representing each cell’s transcriptional progression toward the terminal state.
Slingshot which is one of the methods suggested for lineage reconstruction and pseudotime inference from single-cell gene expression data. In this notebook, we will illustrate an example workflow for cell lineage and pseudotime inference using Slingshot. The notebook is inspired by Slingshot's vignette and modified to demonstrate how the tool works on BioTuring's platform.
BioTuring
Single-cell RNA sequencing methods can profile the transcriptomes of single cells but cannot preserve spatial information. Conversely, spatial transcriptomics assays can profile spatial regions in tissue sections but do not have single-cell resolution.
Here, Runmin Wei (Siyuan He, Shanshan Bai, Emi Sei, Min Hu, Alastair Thompson, Ken Chen, Savitri Krishnamurthy & Nicholas E. Navin) developed a computational method called CellTrek that combines these two datasets to achieve single-cell spatial mapping through coembedding and metric learning approaches. They benchmarked CellTrek using simulation and in situ hybridization datasets, which demonstrated its accuracy and robustness.
They then applied CellTrek to existing mouse brain and kidney datasets and showed that CellTrek can detect topological patterns of different cell types and cell states. They performed single-cell RNA sequencing and spatial transcriptomics experiments on two ductal carcinoma in situ tissues and applied CellTrek to identify tumor subclones that were restricted to different ducts, and specific T-cell states adjacent to the tumor areas.
BioTuring
Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation—the assignment of cell type or cell state to each sequenced cell—is a challenge, especially identifying tumor cells within single-cell or spatial sequencing experiments.
Here, we propose ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single-cell level. We test ikarus on multiple single-cell datasets, showing that it achieves high sensitivity and specificity in multiple experimental contexts.
**InferCNV** is a Bayesian method, which agglomerates the expression signal of genomically adjointed genes to ascertain whether there is a gain or loss of a certain larger genomic segment. We have used **inferCNV** to call copy number variations in all samples used in the manuscript.
Trends
BioTuring
scGen is a generative model to predict single-cell perturbation response across cell types, studies and species (Nature Methods, 2019). scGen is implemented using the scvi-tools framework.
What you can do with scGen:
Train on a dataset with multiple cell types and conditions and predict the perturbation effect on the cell type which you only have in one condition. This scenario can be extended to multiple species where you want to predict the effect of a specific species using another or all the species.
Train on a dataset where you have two conditions (e.g. control and perturbed) and predict on second dataset with similar genes.
Remove batch effect on labeled data. In this scenario you need to provide cell_type and batch labels to the method. Note that batch_removal does not require all cell types to be present in all datasets (batches). If you have dataset specific cell type it will preserved as before.
We assume there exist two conditions in you dataset (e.g. control and perturbed). You can train the model and with your data and predict the perturbation for the cell type/species of interest.
We recommend to use normalized data for the training. A simple example for normalization can be performed using scanpy