Comparing the Characteristics of Microglia Preparations Generated Using Different Human iPSC-Based Differentiation Methods to Model Neurodegenerative Diseases
Bibliographic record
Abstract
As the resident immune cells of the healthy nervous system, homeostatic microglia can rapidly become activated in response to injury/disease. Dysregulated microglia activation is a hallmark of nervous system disorders including neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and Alzheimer's disease. The elucidation of the biological and pathological roles of microglia has recently benefitted from the development of microglia-like cells using human induced pluripotent stem cell (iPSC)-based approaches. The success of iPSC-derived microglia preparations as a disease-relevant model system depends on their representation of the in vivo spatial and temporal heterogeneity of microglia under pathological conditions. Little is currently known about the potential of human iPSC-derived microglia generated using different methods for the study of neurodegenerative diseases. We compared the transcriptomes of human iPSC-derived microglia generated using two frequently used in vitro differentiation methods to determine whether separate strategies can generate microglia with distinct transcriptional signatures in vitro. We show that microglia derived using different differentiation methods display distinct maturation characteristics after equivalent times in culture. We also reveal that iPSC-derived microglia preparations generated using these two methods are composed of different subpopulations with transcriptomic signatures resembling those of in vivo regionally distinct microglia subtypes, specifically white-matter and gray-matter microglia. These findings highlight the need to better characterize the subtype composition of each microglia preparation prior to its use to model neurodegenerative diseases.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".