Comparing the potential of microglia preparations generated using different human iPSC-based differentiation methods to model microglia-mediated mechanisms of amyotrophic lateral sclerosis pathophysiology
Bibliographic record
Abstract
Abstract Microglia are the resident immune cells of nervous system. In healthy conditions, microglia actively patrol neural tissues in a homeostatic state, which can rapidly change to an activated state in response to local injury/disease. Dysregulated microglia activation is a hallmark of disorders and diseases of the nervous system, including motor neuron diseases such as amyotrophic lateral sclerosis (ALS). The elucidation of the roles of microglia in human biology and disease has recently benefitted from the development of human induced pluripotent stem cell (iPSC)-based approaches to generate microglia-like cells. Microglia represent a heterogeneous group of cells with spatial diversity in both health and disease. This situation poses a considerable challenge along the path towards establishing the most pathologically-relevant human iPSC-derived microglia preparations to investigate the complex roles of microglia in ALS and other neurological diseases. The success of these approaches must account for microglia diversity in different regions of the brain and spinal cord. In this study, we compared the transcriptomes of human iPSC-derived microglia generated using different methods to determine whether or not separate strategies can be used to generate microglia with distinct transcriptional signatures in vitro . We show that microglia derived using two different methods display distinct in vitro maturation characteristics. We also reveal that different derivation methods give rise to preparations comprising human microglia with distinct transcriptomic signatures resembling the gene profiles of specific microglia subpopulations in vivo . These findings suggest that a careful, and coordinated, implementation of multiple microglia differentiation methods from human iPSCs can be an effective approach towards the goal of generating multiple microglia subtypes that will offer enhanced model systems to account for microglia heterogeneity in vivo . Spatially-defined human iPSC-derived microglia would represent an enhanced tool to study the multiple levels of involvement of microglia in mechanisms of motor neuron degeneration in ALS, as well as other neurological diseases and disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".