In vitro Quantitative Imaging Assay for Phagocytosis of Dead Neuroblastoma Cells by iPSC-Macrophages
Bibliographic record
Abstract
Microglia orchestrate neuroimmune responses in several neurodegenerative diseases, including Parkinson's disease and Alzheimer's disease. Microglia clear up dead and dying neurons through the process of efferocytosis, a specialized form of phagocytosis. The phagocytosis function can be disrupted by environmental or genetic risk factors that affect microglia. This paper presents a rapid and simple in vitro microscopy protocol for studying microglial efferocytosis in an induced pluripotent stem cell (iPSC) model of microglia, using a human neuroblastoma cell line (SH-SY5Y) labeled with a pH-sensitive dye for the phagocytic cargo. The procedure results in a high yield of dead neuroblastoma cells, which display surface phosphatidylserine, recognized as an "eat-me" signal by phagocytes. The 96-well plate assay is suitable for live-cell time-lapse imaging, or the plate can be successfully fixed prior to further processing and quantified by high-content microscopy. Fixed-cell high-content microscopy enables the assay to be scaled up for screening of small molecule inhibitors or assessing the phagocytic function of genetic variant iPSC lines. While this assay was developed to study phagocytosis of whole dead neuroblastoma cells by iPSC-macrophages, the assay can be easily adapted for other cargoes relevant to neurodegenerative diseases, such as synaptosomes and myelin, and other phagocytic cell types.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".