No abstract

Back and neck pain are significant healthcare burdens that are commonly associated with pathologies of the intervertebral disc (IVD). The poor understanding of the cellular heterogeneity within the IVD makes it difficult to develop regenerative IVD therapies. To address this gap, we developed an atlas of bovine (Bos taurus) caudal IVDs using single-cell RNA-sequencing (scRNA-seq). Unsupervised clustering resolved 15 unique clusters, which we grouped into the following annotated partitions: nucleus pulposus (NP), outer annulus fibrosus (oAF), inner AF (iAF), notochord, muscle, endothelial, and immune cells. Analyzing the pooled gene expression profiles of the NP, oAF, and iAF partitions allowed us to identify novel markers for NP (CP, S100B

Characterizing the tumor microenvironment is crucial in order to improve responsiveness to immunotherapy and develop new therapeutic strategies. The fraction of different cell-types in the tumor microenvironment can be estimated based on transcriptomic profiling of bulk tumor data via deconvolution algorithms. One class of such algorithms, known as reference-based, rely on a reference signature containing gene expression data for various cell-types. The limitation of these methods is that such a signature is derived from the gene expression of pure cell-types, which might not be consistent with the transcriptomic profiling in solid tumors. On the other hand, reference-free methods usually require only a set of cell-specific markers to perform deconvolution; however, once the different components have been estimated from the data, their labeling can be problematic. To overcome these limitations, we propose BayesDeBulk - a new reference-free Bayesian method for bulk deconvolution based on gene expression data. Given a list of markers expressed in each cell-type (cell-specific markers), a repulsive prior is placed on the mean of gene expression in different cell-types to ensure that cell-specific markers are upregulated in a particular component. Contrary to existing reference-free methods, the labeling of different components is decided a priori through a repulsive prior. Furthermore, the advantage over reference-based algorithms is that the cell fractions as well as the gene expression of different cells are estimated from the data, simultaneously. Given its flexibility, BayesDeBulk can be utilized to perform bulk deconvolution beyond transcriptomic data, based on other data types such as proteomic profiles or the integration of both transcriptomic and proteomic profiles.

We introduce UniCell: Deconvolve Base (UCDBase), a pre-trained, interpretable, deep learning model to deconvolve cell type fractions and predict cell identity across Spatial, bulk-RNA-Seq, and scRNA-Seq datasets without contextualized reference data. UCD is trained on 10 million pseudo-mixtures from a fully-integrated scRNA-Seq training database comprising over 28 million annotated single cells spanning 840 unique cell types from 898 studies. We show that our UCDBase and transfer-learning models achieve comparable or superior performance on in-silico mixture deconvolution to existing, reference-based, state-of-the-art methods. Feature attribute analysis uncovers gene signatures associated with cell-type specific inflammatory-fibrotic responses in ischemic kidney injury, discerns cancer subtypes, and accurately deconvolves tumor microenvironments. UCD identifies pathologic changes in cell fractions among bulk-RNA-Seq data for several disease states. Applied to lung cancer scRNA-Seq data, UCD annotates and distinguishes normal from cancerous cells. Overall, UCD enhances transcriptomic data analysis, aiding in assessment of cellular and spatial context.

Conflicts of interest statement: The authors declare no potential conflicts of interest. For information, NC and MJM are developing and validating the technical and clinical performance of the non-invasive blood-based miRNA test described in this manuscript. RA is assisting NC and MJM in translating the test into clinical practice. HA, CB, DC, MF, and KT worked with RA to identify potential healthcare savings following future implementation of the miRNA test.