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264.6: Simultaneous snRNA&ATACseq characterizes specific innate immune memory.

2024· article· en· W4402797767 on OpenAlexaff
Jason Ossart, Matthieu Heitz, Steven M. Sanders, Neda Feizi, Mohamad Zaidan, Camila Macedo, Mouhamad Al Moussawy, S. Hajar Masri, Amanda Williams, E. Wyllis, Aravind Cherukuri, Martin H. Oberbarnscheidt, Diana Metes, Fadi Lakkis, Geoffrey Schiebinger, Khodor I. Abou‐Daya

Bibliographic record

VenueTransplantation · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInnate immune systemImmunological memoryImmune systemNeuroscienceBiologyImmunologyImmunity

Abstract

fetched live from OpenAlex

Purpose: Murine monocytes require A-type paired immunoglobulin-like receptors (PIR-A) to specifically recognize and acquire memory to major histocompatibility complex I antigens. Beyond the need for PIR-A, little is known about the mechanisms of specific innate immune memory. In this study, we aim to explore possible mechanisms of monocyte memory by investigating the epigenetic and transcriptional changes that specifically occur after allogeneic stimulation in splenic monocytes. Methods: B6.RAG-/- γc-/- (BRG) mice were immunized by intraperitoneal injection of 20 million Balb/c irradiated splenocytes. Splenic monocytes were FACS sorted at 0-, 3-, 7- and 28-days post-immunization. Simultaneous snRNAseq and snATACseq was then performed. To identify the changes that are specific to allo-stimulation and related to monocyte memory, monocytes were also sorted and sequenced from BRG mice injected with irradiated B6 splenocytes and allo-immunized BRG PIR-A-/- mice at day 7 after injection. PBMCs were collected from 12 transplant patients with or without rejection. ScRNAseq was performed on the PBMCs after depletion of T and B cells. Results: Weighted nearest neighbor UMAP represented 4 visually distinct cell neighborhoods (N). N1 and N2 increased in abundance after allo-stimulation. Differential gene expression analysis revealed that N1 highly expressed genes encoding cell cycle proteins, Ly-6C, and PIR-A. Clustering resulted in the division of N2 into 3 clusters (C1-C3). Flow cytometric analysis which included EDU pulse-chase confirmed the patterns seen in the sequencing data. Pseudotime multiomic trajectory inference using Multivelo revealed an influx of cells from N1 to N2 at D7. Real-time multiomic trajectory inference using Waddington OT supported that N1 is the starting state for memory formation in response to allogeneic non-self. Real time and pseudotime trajectory inference suggested that the differentiation pathway connects N1 to N2 then splits to N3 and N4. Static and dynamic gene regulatory network inference using Dictys revealed key transcription factors in N1 for monocyte memory formation and response. Similar findings were uncovered in the transcriptomic landscape of monocytes from transplant patients’ PBMCs. Conclusion: Specific Innate Immune Memory is sourced from a monocyte progenitor subset specifically responding to allo-stimulation with unique transcriptional and epigenetic changes which then pass down to its progeny. 2023 American Society of Transplantation Career Transition Grant (Grant #998676). NIAID R01 AI172973. NIAID R01 AI099465.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.256
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
Admission routes1
Has abstractyes

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