Proteome analysis of tissues by mass spectrometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tissues and biofluids are important sources of information used for the detection of diseases and decisions on patient therapies. There are several accepted methods for preservation of tissues, among which the most popular are fresh‐frozen and formalin‐fixed paraffin embedded methods. Depending on the preservation method and the amount of sample available, various specific protocols are available for tissue processing for subsequent proteomic analysis. Protocols are tailored to answer various biological questions, and as such vary in lysis and digestion conditions, as well as duration. The existence of diverse tissue‐sample protocols has led to confusion in how to choose the best protocol for a given tissue and made it difficult to compare results across sample types. Here, we summarize procedures used for tissue processing for subsequent bottom‐up proteomic analysis. Furthermore, we compare protocols for their variations in the composition of lysis buffers, digestion procedures, and purification steps. For example, reports have shown that lysis buffer composition plays an important role in the profile of extracted proteins: the most common are tris(hydroxymethyl)aminomethane, radioimmunoprecipitation assay, and ammonium bicarbonate buffers. Although, trypsin is the most commonly used enzyme for proteolysis, in some protocols it is supplemented with Lys‐C and/or chymotrypsin, which will often lead to an increase in proteome coverage. Data show that the selection of the lysis procedure might need to be tissue‐specific to produce distinct protocols for individual tissue types. Finally, selection of the procedures is also influenced by the amount of sample available, which range from biopsies or the size of a few dozen of mm 2 obtained with laser capture microdissection to much larger amounts that weight several milligrams.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it