Identification of new markers associated with long-term efficacy in patients treated with CAR T cells is a current medical need, particularly in diseases such as multiple myeloma. In this study, we address the impact of CAR density on the functionality of BCMA CAR T cells. Functional and transcriptional studies demonstrate that CAR T cells with high expression of the CAR construct show an increased tonic signaling with up-regulation of exhaustion markers and increased in vitro cytotoxicity but a decrease in in vivo BM infiltration. Characterization of gene regulatory networks using scRNA-seq identified regulons associated to activation and exhaustion up-regulated in CARHigh T cells, providing mechanistic insights behind differential functionality of these cells. Last, we demonstrate that patients treated with CAR T cell products enriched in CARHigh T cells show a significantly worse clinical response in several hematological malignancies. In summary, our work demonstrates that CAR density plays an important role in CAR T activity with notable impact on clinical response.

Motivation An important step in the transcriptomic analysis of individual cells involves manually determining the cellular identities. To ease this labor-intensive annotation of cell-types, there has been a growing interest in automated cell annotation, which can be achieved by training classification algorithms on previously annotated datasets. Existing pipelines employ dataset integration methods to remove potential batch effects between source (annotated) and target (unannotated) datasets. However, the integration and classification steps are usually independent of each other and performed by different tools. We propose JIND (joint integration and discrimination for automated single-cell annotation), a neural-network-based framework for automated cell-type identification that performs integration in a space suitably chosen to facilitate cell classification. To account for batch effects, JIND performs a novel asymmetric alignment in which unseen cells are mapped onto the previously learned latent space, avoiding the need of retraining the classification model for new datasets. JIND also learns cell-type-specific confidence thresholds to identify cells that cannot be reliably classified. Results We show on several batched datasets that the joint approach to integration and classification of JIND outperforms in accuracy existing pipelines, and a smaller fraction of cells is rejected as unlabeled as a result of the cell-specific confidence thresholds. Moreover, we investigate cells misclassified by JIND and provide evidence suggesting that they could be due to outliers in the annotated datasets or errors in the original approach used for annotation of the target batch.

Single-cell RNA-Sequencing has the potential to provide deep biological insights by revealing complex regulatory interactions across diverse cell phenotypes at single-cell resolution. However, current single-cell gene regulatory network inference methods produce a single regulatory network per input dataset, limiting their capability to uncover complex regulatory relationships across related cell phenotypes. We present SimiC, a single-cell gene regulatory inference framework that overcomes this limitation by jointly inferring distinct, but related, gene regulatory dynamics per phenotype. We show that SimiC uncovers key regulatory dynamics missed by previously proposed methods across a range of systems, both model and non-model alike. In particular, SimiC was able to uncover CAR T cell dynamics after tumor recognition and key regulatory patterns on a regenerating liver, and was able to implicate glial cells in the generation of distinct behavioral states in honeybees. SimiC hence establishes a new approach to quantitating regulatory architectures between distinct cellular phenotypes, with far-reaching implications for systems biology.

Myelodysplastic syndromes (MDS) are hematopoietic stem cell (HSC) malignancies characterized by ineffective hematopoiesis, with increased incidence in older individuals. Here we analyze the transcriptome of human HSCs purified from young and older healthy adults, as well as MDS patients, identifying transcriptional alterations following different patterns of expression. While aging-associated lesions seem to predispose HSCs to myeloid transformation, disease-specific alterations may trigger MDS development. Among MDS-specific lesions, we detect the upregulation of the transcription factor DNA Damage Inducible Transcript 3 (DDIT3). Overexpression of DDIT3 in human healthy HSCs induces an MDS-like transcriptional state, and dyserythropoiesis, an effect associated with a failure in the activation of transcriptional programs required for normal erythroid differentiation. Moreover, DDIT3 knockdown in CD34+ cells from MDS patients with anemia is able to restore erythropoiesis. These results identify DDIT3 as a driver of dyserythropoiesis, and a potential therapeutic target to restore the inefficient erythroid differentiation characterizing MDS patients. © 2022, The Author(s).

Background: Tobacco is the main risk factor for developing lung cancer. Yet, some heavy smokers do not develop lung cancer at advanced ages while others develop it at young ages. Here, we assess for the first time the genetic background of these clinically relevant extreme phenotypes using whole exome sequencing (WES). Methods: We performed WES of germline DNA from heavy smokers who either developed lung adenocarcinoma at an early age ( extreme cases, n=50) or did not present lung adenocarcinoma or other tumors at an advanced age (extreme controls, n=50). We selected non-synonymous variants located in exonic regions and consensus splice sites of the genes that showed significantly different allelic frequencies between both cohorts. We validated our results in all the additional extreme cases (i.e., heavy smokers who developed lung adenocarcinoma at an early age) available from The Cancer Genome Atlas (TCGA). Results: The mean age for the extreme cases and controls was respectively 49.7 and 77.5 years. Mean tobacco consumption was 43.6 and 56.8 pack-years. We identified 619 significantly different variants between both cohorts, and we validated 108 of these in extreme cases selected from TCGA. Nine validated variants, located in relevant cancer related genes, such as PARP4, HLA-A or NQO1, among others, achieved statistical significance in the False Discovery Rate test. The most significant validated variant (P=4.48x10(-5)) was located in the tumor-suppressor gene ALPK2. Conclusions: We describe genetic variants associated with extreme phenotypes of high and low risk for the development of tobacco-induced lung adenocarcinoma. Our results and our strategy may help to identify high-risk subjects and to develop new therapeutic strategies.

The protein detection and quantification using high-throughput proteomic technologies is still challenging due to the stochastic nature of the peptide selection in the mass spectrometer, the difficulties in the statistical analysis of the results and the presence of degenerated peptides. However, considering in the analysis only those peptides that could be detected by mass spectrometry, also called proteotypic peptides, increases the accuracy of the results. Several approaches have been applied to predict peptide detectability based on the physicochemical properties of the peptides. In this manuscript, we present DeepMSPeptide, a bioinformatic tool that uses a deep learning method to predict proteotypic peptides exclusively based on the peptide amino acid sequences.

The Human Proteome Project (HPP) is leading the international effort to characterize the human proteome. Although the main goal of this project was first focused on the detection of missing proteins, a new challenge arose from the need to assign biological functions to the uncharacterized human proteins and describe their implications in human diseases. Not only the proteins with experimental evidence (uPE1 proteins) but also the uncharacterized missing proteins (uMPs) were the objects of study in this challenge, neXt-CP50. In this work, we developed a new bioinformatic approach to infer biological annotations for the uPE1 proteins and uMPs based on a "guilt-by-association" analysis using public RNA-Seq data sets. We used the correlation of these proteins with the well-characterized PE1 proteins to construct a network. In this way, we applied the PageRank algorithm to this network to identify the most relevant nodes, which were the biological annotations of the uncharacterized proteins. All of the generated information was stored in a database. In addition, we implemented the web application UPEFinder (https://upefinder. proteored.org ) to facilitate the access to this new resource. This information is especially relevant for the researchers of the HPP who are interested in the generation and validation of new hypotheses about the functions of these proteins. Both the database and the web application are publicly available (https://github.com/tibioinformat/UPEfinder).