Heuristic bias in stem cell biology
Abstract When studying purified hematopoietic stem cells, the urge for mechanisms and reductionist approaches appears to be overwhelming. The prime focus of the field has recently been on the study of highly purified hematopoietic stem cells using various lineage and stem cell-specific markers, all...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-08-01
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| Series: | Stem Cell Research & Therapy |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13287-019-1355-1 |
| _version_ | 1828751577186304000 |
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| author | Peter Quesenberry Theo Borgovan Chibuikem Nwizu Mark Dooner Laura Goldberg |
| author_facet | Peter Quesenberry Theo Borgovan Chibuikem Nwizu Mark Dooner Laura Goldberg |
| author_sort | Peter Quesenberry |
| collection | DOAJ |
| description | Abstract When studying purified hematopoietic stem cells, the urge for mechanisms and reductionist approaches appears to be overwhelming. The prime focus of the field has recently been on the study of highly purified hematopoietic stem cells using various lineage and stem cell-specific markers, all of which adequately and conveniently fit the established hierarchical stem cell model. This methodology is tainted with bias and has led to incomplete conclusions. Much of our own work has shown that the purified hematopoietic stem cell, which has been so heavily studied, is not representative of the total population of hematopoietic stem cells and that rather than functioning within a hierarchical model of expansion the true hematopoietic stem cell is one that is actively cycling through various differentiation potentials within a dynamic continuum. Additional work with increased emphasis on studying whole populations and direct mechanistic studies to these populations is needed. Furthermore, the most productive studies may well be mechanistic at the cellular or tissue levels. Lastly, the application of robust machine learning algorithms may provide insight into the dynamic variability and flux of stem cell fate and differentiation potential. |
| first_indexed | 2024-12-10T20:55:26Z |
| format | Article |
| id | doaj.art-0606ab1abc9140769fd4ca5e1ddf54f9 |
| institution | Directory Open Access Journal |
| issn | 1757-6512 |
| language | English |
| last_indexed | 2024-12-10T20:55:26Z |
| publishDate | 2019-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Stem Cell Research & Therapy |
| spelling | doaj.art-0606ab1abc9140769fd4ca5e1ddf54f92022-12-22T01:33:58ZengBMCStem Cell Research & Therapy1757-65122019-08-011011310.1186/s13287-019-1355-1Heuristic bias in stem cell biologyPeter Quesenberry0Theo Borgovan1Chibuikem Nwizu2Mark Dooner3Laura Goldberg4Division of Hematology and Oncology, Rhode Island Hospital, The Warren Alpert Medical School of Brown UniversityDivision of Hematology and Oncology, Rhode Island Hospital, The Warren Alpert Medical School of Brown UniversityDivision of Hematology and Oncology, Rhode Island Hospital, The Warren Alpert Medical School of Brown UniversityDivision of Hematology and Oncology, Rhode Island Hospital, The Warren Alpert Medical School of Brown UniversityDivision of Hematology and Oncology, Rhode Island Hospital, The Warren Alpert Medical School of Brown UniversityAbstract When studying purified hematopoietic stem cells, the urge for mechanisms and reductionist approaches appears to be overwhelming. The prime focus of the field has recently been on the study of highly purified hematopoietic stem cells using various lineage and stem cell-specific markers, all of which adequately and conveniently fit the established hierarchical stem cell model. This methodology is tainted with bias and has led to incomplete conclusions. Much of our own work has shown that the purified hematopoietic stem cell, which has been so heavily studied, is not representative of the total population of hematopoietic stem cells and that rather than functioning within a hierarchical model of expansion the true hematopoietic stem cell is one that is actively cycling through various differentiation potentials within a dynamic continuum. Additional work with increased emphasis on studying whole populations and direct mechanistic studies to these populations is needed. Furthermore, the most productive studies may well be mechanistic at the cellular or tissue levels. Lastly, the application of robust machine learning algorithms may provide insight into the dynamic variability and flux of stem cell fate and differentiation potential.http://link.springer.com/article/10.1186/s13287-019-1355-1Stem cell hierarchyStem cell continuumRepresentative heuristicsAvailability heuristicsHeuristic bias |
| spellingShingle | Peter Quesenberry Theo Borgovan Chibuikem Nwizu Mark Dooner Laura Goldberg Heuristic bias in stem cell biology Stem Cell Research & Therapy Stem cell hierarchy Stem cell continuum Representative heuristics Availability heuristics Heuristic bias |
| title | Heuristic bias in stem cell biology |
| title_full | Heuristic bias in stem cell biology |
| title_fullStr | Heuristic bias in stem cell biology |
| title_full_unstemmed | Heuristic bias in stem cell biology |
| title_short | Heuristic bias in stem cell biology |
| title_sort | heuristic bias in stem cell biology |
| topic | Stem cell hierarchy Stem cell continuum Representative heuristics Availability heuristics Heuristic bias |
| url | http://link.springer.com/article/10.1186/s13287-019-1355-1 |
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