scholarly journals Abstract 3135: Single-cell RNA sequencing identifies macrophage-specific expression signatures associated with phagocytosis of multiple myeloma after treatment with cIAP antagonist

Author(s):  
Nicholas Banovich ◽  
Martin Boateng ◽  
Marta Chesi ◽  
Leif Bergsagel ◽  
Jonathan Keats
2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Rabah Dabouz ◽  
Colin W. H. Cheng ◽  
Pénélope Abram ◽  
Samy Omri ◽  
Gael Cagnone ◽  
...  

Abstract Background Inflammation and particularly interleukin-1β (IL-1β), a pro-inflammatory cytokine highly secreted by activated immune cells during early AMD pathological events, contribute significantly to retinal neurodegeneration. Here, we identify specific cell types that generate IL-1β and harbor the IL-1 receptor (IL-1R) and pharmacologically validate IL-1β’s contribution to neuro-retinal degeneration using the IL-1R allosteric modulator composed of the amino acid sequence rytvela (as well as the orthosteric antagonist, Kineret) in a model of blue light–induced retinal degeneration. Methods Mice were exposed to blue light for 6 h and sacrificed 3 days later. Mice were intraperitoneally injected with rytvela, Kineret, or vehicle twice daily for 3 days. The inflammatory markers F4/80, NLRP3, caspase-1, and IL-1β were assessed in the retinas. Single-cell RNA sequencing was used to determine the cell-specific expression patterns of retinal Il1b and Il1r1. Macrophage-induced photoreceptor death was assessed ex vivo using retinal explants co-cultured with LPS-activated bone marrow–derived macrophages. Photoreceptor cell death was evaluated by the TUNEL assay. Retinal function was assessed by flash electroretinography. Results Blue light markedly increased the mononuclear phagocyte recruitment and levels of inflammatory markers associated with photoreceptor death. Co-localization of NLRP3, caspase-1, and IL-1β with F4/80+ mononuclear phagocytes was clearly detected in the subretinal space, suggesting that these inflammatory cells are the main source of IL-1β. Single-cell RNA sequencing confirmed the immune-specific expression of Il1b and notably perivascular macrophages in light-challenged mice, while Il1r1 expression was found primarily in astrocytes, bipolar, and vascular cells. Retinal explants co-cultured with LPS/ATP-activated bone marrow–derived macrophages displayed a high number of TUNEL-positive photoreceptors, which was abrogated by rytvela treatment. IL-1R antagonism significantly mitigated the inflammatory response triggered in vivo by blue light exposure, and rytvela was superior to Kineret in preserving photoreceptor density and retinal function. Conclusion These findings substantiate the importance of IL-1β in neuro-retinal degeneration and revealed specific sources of Il1b from perivascular MPs, with its receptor Ilr1 being separately expressed on surrounding neuro-vascular and astroglial cells. They also validate the efficacy of rytvela-induced IL-1R modulation in suppressing detrimental inflammatory responses and preserving photoreceptor density and function in these conditions, reinforcing the rationale for clinical translation.


Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 240 ◽  
Author(s):  
Prashant N. M. ◽  
Hongyu Liu ◽  
Pavlos Bousounis ◽  
Liam Spurr ◽  
Nawaf Alomran ◽  
...  

With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, the estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate the allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10×Genomics Chromium platform. We analyzed 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), sequenced to an average of 150K sequencing reads per cell (more than 4 billion scRNA-seq reads in total). High-quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimated the expressed variant allele fraction (VAFRNA) from SNV-aware alignments and analyzed its variance and distribution (mono- and bi-allelic) at different minimum sequencing read thresholds. Our analysis shows that when assessing positions covered by a minimum of three unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at a threshold of 10 reads, nearly 90% of the SNVs are bi-allelic. In addition, our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3′-based library generation protocol of 10×Genomics scRNA-seq data can be informative in SNV-based studies, including analyses of transcriptional kinetics.


2019 ◽  
Vol 15 ◽  
pp. P1258-P1258 ◽  
Author(s):  
Tulsi Patel ◽  
Troy Carnwath ◽  
Laura Lewis-Tuffin ◽  
Mariet Allen ◽  
Sarah J. Lincoln ◽  
...  

2021 ◽  
Vol 21 ◽  
pp. S74-S75
Author(s):  
Mattia D’Agostino ◽  
Maeva Fincker ◽  
Cristina Panaroni ◽  
Ashish Yeri ◽  
Pingping Mao ◽  
...  

Nature Cancer ◽  
2020 ◽  
Vol 1 (5) ◽  
pp. 493-506 ◽  
Author(s):  
Oksana Zavidij ◽  
Nicholas J. Haradhvala ◽  
Tarek H. Mouhieddine ◽  
Romanos Sklavenitis-Pistofidis ◽  
Songjie Cai ◽  
...  

2019 ◽  
Author(s):  
Nicholas J. Haradhvala ◽  
Oksana Zavidij ◽  
Tarek H. Mouhieddine ◽  
Romanos Sklavenitis-Pistofidis ◽  
Jihye Park ◽  
...  

2019 ◽  
Vol 19 (10) ◽  
pp. e27
Author(s):  
Oksana Zavidij ◽  
Nicholas J. Haradhvala ◽  
Tarek Mouhieddine ◽  
Romanos Sklavenitis-Pistofidis ◽  
Michael P. Agius ◽  
...  

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Kristiyan Kanev ◽  
Patrick Roelli ◽  
Ming Wu ◽  
Christine Wurmser ◽  
Mauro Delorenzi ◽  
...  

AbstractSingle-cell RNA sequencing in principle offers unique opportunities to improve the efficacy of contemporary T-cell based immunotherapy against cancer. The use of high-quality single-cell data will aid our incomplete understanding of molecular programs determining the differentiation and functional heterogeneity of cytotoxic T lymphocytes (CTLs), allowing for optimal therapeutic design. So far, a major obstacle to high depth single-cell analysis of CTLs is the minute amount of RNA available, leading to low capturing efficacy. Here, to overcome this, we tailor a droplet-based approach for high-throughput analysis (tDrop-seq) and a plate-based method for high-performance in-depth CTL analysis (tSCRB-seq). The latter gives, on average, a 15-fold higher number of captured transcripts per gene compared to droplet-based technologies. The improved dynamic range of gene detection gives tSCRB-seq an edge in resolution sensitive downstream applications such as graded high confidence gene expression measurements and cluster characterization. We demonstrate the power of tSCRB-seq by revealing the subpopulation-specific expression of co-inhibitory and co-stimulatory receptor targets of key importance for immunotherapy.


2020 ◽  
Author(s):  
Jared Brown ◽  
Zijian Ni ◽  
Chitrasen Mohanty ◽  
Rhonda Bacher ◽  
Christina Kendziorski

AbstractMotivationNormalization to remove technical or experimental artifacts is critical in the analysis of single-cell RNA-sequencing experiments, even those for which unique molecular identifiers (UMIs) are available. The majority of methods for normalizing single-cell RNA-sequencing data adjust average expression in sequencing depth, but allow the variance and other properties of the gene-specific expression distribution to be non-constant in depth, which often results in reduced power and increased false discoveries in downstream analyses. This problem is exacerbated by the high proportion of zeros present in most datasets.ResultsTo address this, we present Dino, a normalization method based on a flexible negative-binomial mixture model of gene expression. As demonstrated in both simulated and case study datasets, by normalizing the entire gene expression distribution, Dino is robust to shallow sequencing depth, sample heterogeneity, and varying zero proportions, leading to improved performance in downstream analyses in a number of settings.Availability and implementationThe R package, Dino, is available on GitHub at https://github.com/JBrownBiostat/[email protected], [email protected]


2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Igor Mandric ◽  
Tommer Schwarz ◽  
Arunabha Majumdar ◽  
Kangcheng Hou ◽  
Leah Briscoe ◽  
...  

Abstract Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to directly and simultaneously measure cellular composition and state, which can otherwise only be estimated by applying deconvolution methods to bulk RNA-Seq estimates. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We use simulations starting from one of the largest available real single-cell RNA-Seq data from 120 individuals to also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power which is available in the form of a web tool.


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