The Human Cell Atlas Project by the numbers: Relationship to the Banff Classification
Bibliographic record
Abstract
To the Editor Two recent AJT articles refer to the “step change” that the Human Cell Atlas Project (HCAP) will bring about in transplantation.1Pullen LC The AJT Report: Human Cell Atlas poised to transform our understanding of organs.Am J Transplant. 2018; 18: 1-2Abstract Full Text Full Text PDF PubMed Scopus (4) Google Scholar,2Solez K Fung KC Saliba KA et al.Personal viewpoint: the bridge between transplantation and regenerative medicine. Beginning a new Banff Classification of Tissue Engineering Pathology.Am J Transplant. 2018; 18: 321-327Abstract Full Text Full Text PDF PubMed Scopus (10) Google Scholar Many readers will want to know how big that step will be. On the one hand, HCAP reports are appearing in the top journals, and there are videos and articles about it widely available on social media, with predictions of a 10- to-100 000-fold increase in known cell types and claims such as “Our bodies are made up of least 37 trillion cells, and scientists are teaming up around the world to map every single one of them.”3Weule G. ABC Science –Human Cell Atlas: the plan to map every cell in your body. http://www.abc.net.au/news/science/2017-11-17/human-cell-atlas-the-plan-to-map-every-cell-in-your-body/9127096. Accessed February 8, 2018.Google Scholar On the other hand, some large traditional meetings like the US/Canadian Academy of Pathology meeting and the American Transplant Congress have been entirely silent on the issue of the HCAP. Taking a rigorous approach to the numbers, there is evidence for a least a doubling of known cell types by applying HCAP technologies. Villani et al4Villani AC Satija R Reynolds G Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors.Science. 2017; 356Crossref PubMed Scopus (1210) Google Scholar demonstrated a doubling of cell types within dendritic cells, monocytes, and progenitors (from 6 to 12) when HCAP technologies are applied. Extrapolating from that, a conservative estimate of the impact of HCAP is that it will double the number of known cell types in every organ transplanted. So, for instance, the number of cell types in the kidney would go from 26 to approximately 52. Even at that level, it is something huge that everyone should know about and be making plans for. It is particularly relevant to the new Banff Classification of Tissue Engineering Pathology, as pointed out in a recent personal viewpoint paper.2Solez K Fung KC Saliba KA et al.Personal viewpoint: the bridge between transplantation and regenerative medicine. Beginning a new Banff Classification of Tissue Engineering Pathology.Am J Transplant. 2018; 18: 321-327Abstract Full Text Full Text PDF PubMed Scopus (10) Google Scholar As for the question of how many cells the project plans to characterize, the HCAP White Paper5The HCA Consortium, The Human Cell Atlas White Paper, October 18, 2017. https://www.humancellatlas.org/files/HCA_WhitePaper_18Oct2017. Accessed March 11, 2018.Google Scholar,6Regev A Teichmann SA Lander ES Human Cell Atlas meeting participants. The Human Cell Atlas.Elife. 2017; 6Google Scholar is clear that the intention is to profile 30-100 million cells from healthy controls of both sexes in the first draft of the project and then incorporate the lessons learned from that into creation of a comprehensive atlas of at least 10 billion cells, covering all tissues, organs, and systems. The guiding principle determining how many cells will be analyzed is ”Given a tissue with N discrete cell subsets, the rarest of which is present at proportion P, how many cells k need to be sampled such that at least n cells are recovered in each subset with confidence level C?” (The HCA Consortium,5p21 Box 1). At every step, the HCAP will bring about many important new insights, ultimately changing and making more precise every aspect of transplantation. The new Banff Classification of Tissue Engineering Pathology was first suggested in 2011, and concrete plans to make it happen have been in place since 2017, with the aim to have it completely finalized by 2025.2Solez K Fung KC Saliba KA et al.Personal viewpoint: the bridge between transplantation and regenerative medicine. Beginning a new Banff Classification of Tissue Engineering Pathology.Am J Transplant. 2018; 18: 321-327Abstract Full Text Full Text PDF PubMed Scopus (10) Google Scholar HCAP is an integral part of the new Banff Classification of Tissue Engineering Pathology. HCAP should become part of the mind set of every transplant physician and every transplant pathologist and will need to be central in the joint practical planning of those 2 communities for the future. The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".