Single-cell RNA sequencing highlights the role of distinct natural killer subsets in sporadic amyotrophic lateral sclerosis
Bibliographic record
Abstract
Neuroinflammation plays a major role in amyotrophic lateral sclerosis (ALS), and cumulative evidence suggests that systemic inflammation and the infiltration of immune cells into the brain contribute to this process. However, no study has investigated the role of peripheral blood immune cells in ALS pathophysiology using single-cell RNA sequencing (scRNAseq). We aimed to characterize immune cells from blood and identify ALS-related immune alterations at single-cell resolution. For this purpose, peripheral blood mononuclear cells (PBMC) were isolated from 14 ALS patients and 14 cognitively unimpaired healthy individuals (HC), matched by age and gender, and cryopreserved until library preparation and scRNAseq. We analyzed differences in the proportions of PBMC, gene expression, and cell-cell communication patterns between ALS patients and HC, as well as their association with plasma neurofilament light (NfL) concentrations, a surrogate biomarker for neurodegeneration. Flow cytometry was used to validate alterations in cell type proportions. We identified the expansion of CD56 dim natural killer (NK) cells in ALS (fold change = 2; adj. p -value = 0.0051), mainly driven by a specific subpopulation, NK_2 cells (fold change = 3.12; adj. p -value = 0.0001), which represent a mature and cytotoxic CD56 dim NK subset. Our results revealed extensive gene expression alterations in NK_2 cells, pointing towards the activation of immune response (adj. p -value = 9.2 × 10 − 11 ) and the regulation of lymphocyte proliferation (adj. p -value = 6.46 × 10 − 6 ). We also identified gene expression changes in other immune cells, such as classical monocytes, and distinct CD8 + effector memory T cells which suggested enhanced antigen presentation via major histocompatibility class-II (adj. p -value = 1.23 × 10 − 8 ) in ALS. The inference of cell-cell communication patterns demonstrated that the interaction between HLA-E and CD94:NKG2C from different lymphocytes to NK_2 cells is unique to ALS blood compared to HC. Finally, regression analysis revealed that the proportion of CD56 bright NK cells along with the ALSFRS-r, disease duration, and gender, explained up to 76.4% of the variance in plasma NfL levels. Our results reveal a signature of relevant changes occurring in peripheral blood immune cells in ALS and underscore alterations in the proportion, gene expression, and signaling patterns of a cytotoxic and terminally differentiated CD56 dim NK subpopulation (NK_2), as well as a possible role of CD56 bright NK cells in neurodegeneration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".