No detectable alloreactive transcriptional responses under standard sample preparation conditions during donor-multiplexed single-cell RNA sequencing of peripheral blood mononuclear cells

Abstract

Background Single-cell RNA sequencing (scRNA-seq) provides high-dimensional measurements of transcript counts in individual cells. However, high assay costs and artifacts associated with analyzing samples across multiple sequencing runs limit the study of large numbers of samples. Sample multiplexing technologies such as MULTI-seq and antibody hashing using single-cell multiplexing kit (SCMK) reagents (BD Biosciences) use sample-specific sequence tags to enable individual samples to be sequenced in a pooled format, markedly lowering per-sample processing and sequencing costs while minimizing technical artifacts. Critically, however, pooling samples could introduce new artifacts, partially negating the benefits of sample multiplexing. In particular, no study to date has evaluated whether pooling peripheral blood mononuclear cells (PBMCs) from unrelated donors under standard scRNA-seq sample preparation conditions (e.g., 30 min co-incubation at 4 °C) results in significant changes in gene expression resulting from alloreactivity (i.e., response to non-self). The ability to demonstrate minimal to no alloreactivity is crucial to avoid confounded data analyses, particularly for cross-sectional studies evaluating changes in immunologic gene signatures. Results Here, we applied the 10x Genomics scRNA-seq platform to MULTI-seq and/or SCMK-labeled PBMCs from a single donor with and without pooling with PBMCs from unrelated donors for 30 min at 4 °C. We did not detect any alloreactivity signal between mixed and unmixed PBMCs across a variety of metrics, including alloreactivity marker gene expression in CD4+ T cells, cell type proportion shifts, and global gene expression profile comparisons using Gene Set Enrichment Analysis and Jensen-Shannon Divergence. These results were additionally mirrored in publicly-available scRNA-seq data generated using a similar experimental design. Moreover, we identified confounding gene expression signatures linked to PBMC preparation method (e.g., Trima apheresis), as well as SCMK sample classification biases against activated CD4+ T cells which were recapitulated in two other SCMK-incorporating scRNA-seq datasets. Conclusions We demonstrate that (i) mixing PBMCs from unrelated donors under standard scRNA-seq sample preparation conditions (e.g., 30 min co-incubation at 4 °C) does not cause an allogeneic response, and (ii) that Trima apheresis and PBMC sample multiplexing using SCMK reagents can introduce undesirable technical artifacts into scRNA-seq data. Collectively, these observations establish important benchmarks for future cross-sectional immunological scRNA-seq experiments.

Structural Analysis of SARS-CoV-2 ORF8 Protein: Pathogenic and Therapeutic Implications

“…For example, of the 11,113 ORF8 sequences analyzed by Pereira (2020) , the L84S substitution is the mutation that has been positively selected during the course of the pandemic. In 58 sites with this mutation the change in position 84 from leucine (observed in 85% of the sequences) to serine (observed in 15% of the sequences) stands up ( Vilar and Isom, 2021 ; Zinzula, 2021 ). In the last group, the gene ORF8 ( ORF8 ) has a notable tendency to recombine and undergo deletions that exceed the evolutionary capacity of its analogs in other coronaviruses, facilitating SARS-CoV-2 adaptability to new reservoirs and hosts ( Abdelrahman et al, 2020 ; Zinzula, 2021 ).…”

Natural Barcodes for Longitudinal Single Cell Tracking of Leukemic and Immune Cell Dynamics

“…This can be achieved either through annotation with a genotype reference obtained using WES of purified donor or recipient-derived cells (125,126) or with reference-free approaches that are based on statistical modeling. Tools that implement the latter strategy for scRNA-seq were originally developed for deconvolution of samples from different donors but can be used for assignment of donor and host at single-cell resolution (127)(128)(129)(130). For scWGSseq and scATAC-seq data, a similar approach is possible (131,132), but has not been implemented yet.…”

A precise bare simulation approach to the minimization of some distances. Foundations

“…chip-scale optical circuit type) silicon probes for sub-millisecond deep-brain optical stimulation -e.g. for the purpose of gaining a deeper understanding of the neural code, in Perera et al [290] for the quantification of the level of rationality in supply chain networks, in Pierri et al [294] for the study of growth of malicious/misleading information in some social media diffusion networks, in Rabadan et al [299] for the identification of gene mutations that lead to the genesis and progression of tumors, in Reiter et al [306] for quantifying metastatic phylogenetic diversity, in Van de Sande et al [378] as part of a computational toolbox for single-cell gene regulatory network analysis, in Skinnider et al [337] for the prediction of the chemical structures of genomically encoded antibiotics -in order to find means against the looming global crisis of antibiotic resistance, in Tuo et al [367] for the detection of high-order single nucleotide polymorphism (SNP) interactions, in Uttam et al [370] for predicting the risk of colorectal cancer recurrence and inferring associated tumor microenvironment networks, in Zhang et al [421] for incipient fault (namely, crack) detection, in Zhi et al [425] for the strengthening of information-centric networks against interest flooding attack (IFAs), in Acera Mateos et al [3] for deep-learning classification of SARS-CoV-2 and co-infecting RNA viruses, in Avsec et al [24] for uncovering the motifs and syntax of cis-regulatory sequences in genomics data, in Barennes et al [32] for comparing the accuracy of current T cell receptor sequencing methods employed for the understanding of adaptive immune responses, in Chen et al [79] for clustering high-dimensional microbial data from RNA sequencing, in Chen et al [84] for investigating key aspects of effective vocal social communication, in Koldobskiy et al [193] for investigations of genetic and epigenetic drivers of paediatric acute lymphoblastic leukaemia, in McGinnis et al [258] for evaluating RNA sequencing of pooled blood cell samples, in Mühlroth & Grottke [268] for the detection of emerging trends and technologies through artificial intelligence techniques, in Necci et al [272] for the assessment of protein intrinsic disorder predictions, in Okada et al [277] for the identification of genetic factors that cause individual differences in whole lymphocyte profiles and their changes after vaccination, and in Zhang et al [422] for the learning of functional magnetic resonance imaging (fMRI) time-seri...…”

“…(ia) Let us start with the subcase γ ∈] − ∞, 0[. From the first and the last line of (258), it is clear that the corresponding m−infimum can not be achieved for m ≤ 0; since H γ > 0 one gets the unique minimizer m min = A γ/(1−γ) ). Hence, (48) is established.…”

“…(ic) In the subcase γ ∈]1, ∞[ (cf. the fifth, sixth and seventh line of (258)) it is straightforward to see that the desired infimum can not be achieved for m < 0. Hence, one can proceed analogously to subcase (ia).…”