Differences in the Transcriptomes of Highly Purified Fetal Liver and Adult Bone Marrow Hematopoietic Stem Cells Revealed by Long Serial Analysis of Gene Expression (LongSAGE)
Bibliographic record
Abstract
Abstract Recent improvements in the development of methods for isolating functionally validated populations of nearly pure (>20%) murine hematopoietic stem cells (HSCs) have made it possible to analyze the molecular basis of the properties of these cells with increased precision. One intriguing feature of HSCs is the change they undergo in many of their key properties during development - a change that affects the control of their self-renewal, cycling status, differentiated progeny output and steel factor sensitivity. To investigate how these differences are mediated, we undertook a genome-wide analysis of the transcripts present in highly purified fetal and adult HSCs using an adaptation of the LongSAGE methodology that allows its application to small numbers of cells (10 ng of RNA) by inclusion of an initial PCR amplification step that preserves the transcript repertoire while excluding less than 0.25% of the transcripts. The LongSAGE methodology was adopted because it is sequence-based and thus quantitative and independent of prior knowledge of expressed genes or variations in their hybridization to matching or related cDNAs, ESTs or derived oligos. A suspension of 10,000 lin-Sca-1+CD43+Mac1+ fetal liver (FL) cells (∼20% pure HSCs as determined by 16-week limiting dilution and single cell transplantation experiments) was obtained from embryonic day 14.5 fetuses. From these cells, we generated a 160,000-tag LongSAGE library containing 7865 tags that map uniquely to the mouse genome (using the RefSeq database through DiscoverySpace; www.bcgsc.ca/DiscoverySpace). A suspension of 3700 CD45midlin-Rho-SP cells (∼30% pure HSCs) was isolated from adult mouse bone marrow (BM) and then used to generate a 37,000-tag LongSAGE library (956 uniquely mapped tags). Both of these libraries contained tags identifying transcripts that have been previously reported to be associated with HSCs from FL and/or adult BM, including c-kit, pbx-1, tgf-β, cul-4a, PrP, c-myc, robo1, sox17, as well as a number of Smarc transcripts, ubiquitin ligase transcripts and TNF-related transcripts. As a first test of the utility of the libraries, we looked for differences in the expression of genes that have been broadly associated with differences in cellular proliferative activity. This comparison identified many such tags in the FL HSC library that were absent from the adult BM HSC library, including multiple cyclins (A2, B2, C, D1, D2, D3, E1, F, H, I, J L1, L2), cdc20, cdc5b, and plk, as well as 34 of the top 50 “proliferation” genes identified by Venezia et al (PLoS Biology, 2004) to be selectively expressed in adult BM HSCs that had been stimulated to proliferate. Other transcripts that were present at significantly higher levels in the FL HSC library (95% C.I. using Audic Claverie statistics) included msl2, rbx1, lmo2, pfn1, and 16 members of the tripartite motif protein (trim) family. Conversely, many transcripts for components of the proteosome, involved in nucleic acid binding, and transcripts coding for proteins with receptor activity were present at higher levels (or uniquely) in the adult BM HSC library. Taken together, these findings establish the validity and potential of these permanent HSC transcriptome resources for further investigation of mechanisms that determine the different biology of fetal and adult HSCs.
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 | 0.000 | 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".