Cellular Transcriptional Profiling Reveals Multiple B Cell Differentiation Branching Points in Mus musculus Spleen Exposed to Lipopolysaccharides (LPS) and NP-4-Hydroxy-3- Nitrophenylacetic (NPF) Open Access
Pelia, Ranjit (Spring 2022)
Published
Abstract
Background: Environmental exposure of toxicants has been shown to be detrimental to health. The humoral immune response is an integral component of pathogen recognition and elimination. In which, B cell production is increased and cascades into antibody secreting cells differentiation. These cells are one of the primary defense mechanisms of multicellular organisms, as they can bind, eliminate, decoy, or neutralize the pathogen. There is a paucity in our current understanding of immunological dynamics at a cellular level. The duality of single- cell RNA sequencing (scRNA-seq) and mouse immunity models have enabled researchers to decipher genetic, epigenetic, and transcriptomic mechanisms of action on a molecular scale.
Objective: The transcriptional program driving immune responses and the
humoral homeostasis dynamics remains elusive. We aimed to characterize transcriptional profiling during B-cell differentiation into antibody secreting cells (ASC) using scRNA-seq in order to gain deeper insight into the mechanisms of immunological regulation.
Method: Using the datasets provided by the Scharer lab and published in Nature Communications (2020:11:3989), we employed integrated bioinformatics to assess mouse (Mus musculus) spleen exposed to Lipopolysaccharides (LPS), n=2, and NP-4-Hydroxy-3- Nitrophenylacetic (NPF), n=2, and sequenced using 10X 3-prime scRNA-seq. R packages Seurat, VelocytoR, and the methods described in Nature by Le Manno et al (2018) were utilized for quality control, cell type clustering, and differential kinetics analysis. Lastly, Python based ScVelo was performed on the integrated LPS and NPF samples to calculate latent time, RNA velocity, and gene-cell-type specific trajectories.
Results: Upon sequencing, the LPS samples contained 3312 and 3164 cells and NPF samples contained 1893 and 3330 cells. Upon integration, LPS and NPF were clustered and annotated into three main groups: activated B cells, naïve B cells, and ASCs. Using the ROC for differential expression analysis, there were 4974 in LPS and 2037 in NPF genes specific for the overall subclusters: naïve B, activated B non-ASC, activated B ASC, non-ASC, and ASCs.
Trajectory analysis reveals two distinct B-cell lineages in both LPS and NPF. Latent time showed ASCs to be at a terminal state where B-cells were shown to be proliferating.
Conclusion: Here, we characterized the transcriptomic nature of immune cells in response to pathogens and the consequential differentiation lineages of B-cells. Our results demonstrate insights into the dynamics of immune response and may be translational in the realms of autoimmune illnesses, environmental detriments to health, and our global understanding of pathogen antagonization.
Table of Contents
INTRODUCTION ............................................................................................................................................... 1
METHODS ........................................................................................................................................................ 2
10X 3’(PRIME) SCRNASEQ OF LPS AND NPF STIMULATED MUS MUSCULUS SPLEEN TISSUE: ................................. 2
LOOM FORMATTING AND GENERATION: ........................................................................................................... 2
QUALITY CONTROL AND BATCH EFFECTS ASSESSMENT: ..................................................................................... 2
STATISTICAL PARAMETERS FOR INTEGRATION:.................................................................................................. 3
RESULTS .......................................................................................................................................................... 3
INTEGRATIVE CLUSTERING: .............................................................................................................................. 3
CELL TYPE ANNOTATIONS: ................................................................................................................................ 3
RNA VELOCITY AND PSEUDOTIME ANALYSIS: .................................................................................................... 3
BRANCHING POINTS AND TRAJECTORIES: ......................................................................................................... 4
DISCUSSION: .................................................................................................................................................... 4
TABLES ............................................................................................................................................................ 6
TABLE 1: NUMBER OF GENES AND CELLS FOR EACH MOUSE SPLEEN SAMPLE ...................................................... 6
TABLE 2: PROPORTION OF MTRNA, MITOCHONDRIAL RNA PERCENTAGES PER CELL PER SAMPLE ........................ 6
TABLE 3: PRE- AND POST-FILTERING BASED ON MTRNA PERCENTAGES .............................................................. 6
TABLE 4: CELL CYCLE SPECIFIC ANNOTATION OF PROBABILITY BASED ON G1, G2, AND S SCORES ......................... 6
TABLE 5: NUMBER OF CELLS PER SAMPLE, AFTER FINAL QUALITY CONTROL AND FILTERING ................................ 6
FIGURES .......................................................................................................................................................... 7
FIGURE 1: ANALYTICAL WORKFLOW OF LPS (N=2) AND NPF (N=2) SCRNASEQ DATASETS WITH CONSIDERATION FOR SAMPLE VS TOXICANT SPECIFICITY. THE DYNAMIC HOMEOSTATIC GRAPH WITH T=TIME ON THE X-AXIS DENOTES VARIABLE STATES OF A CELL WITH ADAPTIVE GENE EXPRESSION. ....................................................... 7
FIGURE 2: UMI OR TOTAL NUMBER OF GENES FOR A GIVEN CELL FOR EACH SAMPLE ARE SHOWN ABOVE. LPS 1 . 8
(A) AND LPS 2 (C) SHOWED EXTREMELY CONSISTENT VALUES. WHEREAS, NPF 1 (B) AND NPF 2 (D) WERE SLIGHTLY INCONSISTENT DUE TO VARYING DEGREES IN LOWER RANGES OF EXPRESSION. ................................................ 8
FIGURE 3: THE RATIO OF UNSPLICED TO SPLICED READS ARE SHOWN FOR EACH SAMPLE, LPS 1 ~ 0.18 (A), LPS 2 ~ 0.18 (B), NPF 1 ~ 0.22 (C), AND NPF 2 ~ 0.21 (D). THE GREEN LABELED DIGIT IS THE UNSPLICED- AND THE RED LABELED DIGIT IS THE SPLICED AVERAGE RANK OF CELLS WITH THE X AXIS REPRESENTING THE TOTAL UMI COUNTS. .......................................................................................................................................................... 8
FIGURE 4: CELL CYCLE SCORING, COMPONENT OF SEURAT WAS USED FOR CELL CYCLE SPECIFIC SCORING FOR G1, G2/M, AND S PHASES FOR LPS 1 (A), LPS 2 (B), NPF 1 (C), AND NPF 2 (D). ........................................................... 9
FIGURE 5: INTEGRATED LPS (A) AND NPF (B) SCALES OF TRANSCRIPTION RATE, SPLICING RATE, AND DEGRADATION RATE FOR ALL CELLS IS SHOWN. ................................................................................................ 9
FIGURE 6: VISUALIZATION OF LPS AND NPF CLUSTERS THROUGH UMAP (A, C) AND TSNE (B, D) PERFORMED BY LOUVAIN ALGORITHM WITH MULTIPLE LEVEL REFINEMENT COMPRISED OF N=20 PRINCIPAL COMPONENTS FROM 1 TO 20. ............................................................................................................................................... 10
FIGURE 7: PRINCIPAL COMPONENTS 1 AND 2 SHOWING ALL OF THE CELLS IN LPS (A) AND NPF (B) CLUSTERED BASED ON CELL TYPE ANNOTATIONS. LPS DATASET OF N=5373 CELLS SHOWED N=3312 NAÏVE B, N=1335 ACTB NON-ASC, N=142 ACTB ASC, N=355 ASC, AND N=228 NON-ASC. NPF DATASET OF N=4229 CELLS COMPRISED OF N=2304 NAÏVE B, N=339 ACTB NON-ASC N=385 ACTB ASC, , N=838 NON-ASC, AND N=363 ASC. ........................ 10
FIGURE 8: UMAP AND TSNE SHOWING ALL OF THE CELLS IN LPS (A,C) AND NPF (B,D) CLUSTERED BASED ON CELL TYPE ANNOTATIONS. LPS DATASET OF N=5373 CELLS SHOWED N=3312 NAÏVE B, N=1335 ACTB NON-ASC, N=355 ASC, N=228 NON-ASC, AND N=142 ACTB ASC. NPF DATASET OF N=4229 CELLS COMPRISED OF N=2304 NAÏVE B, N=838 NON ASC, N=385 ACTB ASC, N=339 ACTB NON ASC, AND N=363 ASC. .................................................... 11
FIGURE 9: RNA VELOCITY OF LPS DATASET N=5373 CELLS CONDUCTED WITH DYNAMICAL BAYESIAN MODEL INFERENCES USING PRINCIPAL COMPONENTS 1 TO 20. THE LEFT IMAGE DISPLAYS THE TSNE AND RIGHT SHOWS UMAP OF PROBABILISTIC TRAJECTORIES. ........................................................................................................ 11
FIGURE 10: RNA VELOCITY OF NPF DATASET N=4229 CELLS CONDUCTED WITH DYNAMICAL BAYESIAN MODEL INFERENCES USING PRINCIPAL COMPONENTS 1 TO 20. THE LEFT IMAGE DISPLAYS THE TSNE AND RIGHT SHOWS UMAP OF PROBABILISTIC TRAJECTORIES. ........................................................................................................ 12
FIGURE 11: LPS AND NPF TSNE BASED VELOCITY LENGTH (A, C) AND THE CONFIDENCE (B,D) OF THE VELOCITIES PER CELL GIVEN ON A PROBABILISTIC SCALE.................................................................................................... 12
FIGURE 12: LPS AND NPF UMAP BASED VELOCITY LENGTH (A, C) AND THE CONFIDENCE (B,D) OF THE VELOCITIES PER CELL GIVEN ON A PROBABILISTIC SCALE. .................................................................................................. 13
FIGURE 13: DIFFUSION PSEUDOTIME OF LPS, N=4229 CELLS OVERLAYED ONTO TSNE (A). PARTITION-BASED GRAPHICAL ABSTRACTION (PAGA) OF LPS TSNE (B) SHOWS BINARY LINEAGES FROM NAÏVE B TO ASC CELLS OR ACTIVATED B CELLS. ....................................................................................................................................... 13
FIGURE 14: DIFFUSION PSEUDOTIME OF NPF, N=5373 CELLS OVERLAYED ONTO TSNE (A). PARTITION-BASED GRAPHICAL ABSTRACTION (PAGA) OF NPF TSNE (B) SHOWS BINARY LINEAGES FROM NAÏVE B TO ASC OR ACTIVATED B CELLS. ....................................................................................................................................... 14
BIBLIOGRAPHY ............................................................................................................................................... 15
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Cellular Transcriptional Profiling Reveals Multiple B Cell Differentiation Branching Points in Mus musculus Spleen Exposed to Lipopolysaccharides (LPS) and NP-4-Hydroxy-3- Nitrophenylacetic (NPF) () | 2022-04-28 19:05:07 -0400 |
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