A Stainless Protocol for High Quality RNA Isolation from Laser Capture Microdissected Purkinje Cells in the Human Post-Mortem Cerebellum
Bibliographic record
Abstract
Laser capture microdissection (LCM) is an advantageous tool that allows for the collection of cytologically and/or phenotypically relevant cells or regions from heterogenous tissues. Captured product can be used in a variety of molecular methods for protein, DNA or RNA isolation. However, preservation of RNA from postmortem human brain tissue is especially challenging. Standard visualization techniques for LCM require histologic or immunohistochemical staining procedures that can further degrade RNA. Therefore, we designed a stainless protocol for visualization in LCM with the intended purpose of preserving RNA integrity in post-mortem human brain tissue. The Purkinje cell of the cerebellum is a good candidate for stainless visualization, due to its size and characteristic location. The cerebellar cortex has distinct layers that differ in cell density, making them a good archetype to identify under high magnification microscopy. Purkinje cells are large neurons situated between the granule cell layer, which is a densely cellular network of small neurons, and the molecular layer, which is sparse in cell bodies. Because of this architecture, the use of stainless visualization is feasible. Other organ or cell systems that mimic this phenotype would also be suitable. The stainless protocol is designed to fix fresh-frozen tissue with ethanol and remove lipids with xylene for improved morphological visualization under high magnification light microscopy. This protocol does not account for other fixation methods and is specifically designed for fresh-frozen tissue samples captured using an ultraviolet (UV)-LCM system. Here, we present a full protocol for sectioning and fixing fresh frozen post-mortem human cerebellar tissue and purification of RNA from Purkinje cells isolated by UV-LCM, while preserving RNA quality for subsequent RNA-sequencing. In our hands, this protocol produces exceptional levels of cellular visualization without the need for staining reagents and yields RNA with high RNA integrity numbers (≥8) as needed for transcriptional profiling experiments.
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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 | 0.000 | 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) | 0.000 | 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".