Stochastic founder clones, locked in by feedback competition, set an individual's myeloid set point

Question: How do stochastic differences in hematopoietic stem and progenitor cell output generate stable, individual-specific variation in monocyte and other myeloid lineage proportions in genetically identical mammals?

Answer

Taken together, the retrieved papers support a clone-ensemble model rather than a stem-cell-identity model: stable individual differences in myeloid proportions are not evidence that an animal's hematopoietic stem cells (HSCs) carry a fixed, cell-intrinsic lineage bias, but rather the settled outcome of a stochastic founding condition — which clones seed, how fast they engraft, and where they occupy quiescent/niche space — amplified and then held in place by feedback-regulated competition for size-limited mature compartments, by non-migratory niche occupancy, and by durable-but-reversible transcriptional states that keep clone output from re-randomising each division. Across these papers, the inter-individual difference is therefore an emergent attractor of the whole clone population, which explains why such differences are stably reproducible within an animal yet reset by transplantation, shifted by conditioning, and slowly redrawn with age.

Transplant-assayed lineage bias is largely not a fixed property of individual HSCs

Increasing the number of long-term HSCs transplanted did not alter the between-donor variation in myeloid-to-lymphoid ratios, and the authors concluded that this variation reflects a stochastic process influenced by the irradiated recipient niche rather than cell-intrinsic HSC lineage bias [1]. Young and aged long-term HSCs co-transplanted into the same recipients produced nearly equivalent myeloid output, with no myeloid gene enrichment in aged long-term HSCs, and myeloid skewing instead followed from altering the long-term/short-term HSC ratio and from aged thymic and splenic environments [2]. In single-cell transplantations of over 1,000 purified murine long-term HSCs, apparent lineage biases were reproduced by mathematical modelling only when cell-extrinsic feedback regulation and clonal competition between slow- and fast-engrafting clones for the limited compartment sizes of mature lineages were included [7]. Reconstitution kinetics served as a single unifying metric of HSC potency along which platelet/erythro-myeloid bias gives way to balanced and then lymphoid output [7].

Competition for size-limited compartments converts stochastic founder differences into self-consistent proportions

In hierarchical tissues with homeostatic feedback, the invasion threshold for a clone is set by the equilibrium established by resident clones, so clonal composition becomes history-dependent and clones entering the same compartment exclude each other [19]. Homogeneous single-compartment HSC models reproduce the gradual loss of clonal diversity but fail to reproduce clone size distributions or the long-term persistence of small and late-appearing clones, whereas a stochastic two-compartment model with reversible entry into a niche-bound quiescent state fits across metrics and sustains coexistence of small and large clones [5]. A subset of highly purified long-term HSCs was found to be non-migratory in homeostasis, after platelet depletion, and during ageing, remaining in direct contact with vasculature and LepR+ perivascular cells rather than relocating between niches upon activation [12]. In a model integrating local and systemic inflammatory feedback, global feedback enhanced the resilience of hematopoiesis whereas excessive feedback onto progenitors drove instability and clonal dominance [9].

The stable state is a bounded chaotic regime, not a programmed ratio

Longitudinal sampling of healthy mouse blood revealed coefficients of variation larger than 1, extensive correlations and anticorrelations between cell types within and between lineages, and rare synchronized bursts of proliferation across marrow, blood and spleen [6]. These leukocyte fluctuations were ergodic yet chaotic, with a broad spectrum of characteristic timescales, and a minimal mathematical model captured the fluctuations, correlations and chaos; the correlated fluctuations were reproduced across cohorts of different genetic backgrounds and ages [6]. At the clonal level, long-term chase of more than seventy thousand clones showed that clonal expansion in differentiated stem and progenitor cells, rather than in the most primitive HSCs, drove clonal expansion in the megakaryocyte and myeloid lineages, while HSC-derived clones preserved their fates towards self-renewal or multilineage differentiation [3].

Durable but reversible molecular states prevent the stochastic outcome from re-randomising

Sequential genetic barcoding of 181,695 subclones uncovered bidirectional fate transitions between myeloid-biased and lineage-balanced HSCs, with individual clones showing durable lineage bidirectionality and dynamics favouring progressive accumulation of myeloid-biased HSCs; Hhex was identified as a suppressor of myeloid differentiation whose age-dependent decline enables the shift toward balanced output to be lost [4]. A DNA-barcoding paired-daughter assay over 476 division pairs showed coordinated daughter fates and a bias toward stem-stem and progenitor-progenitor divisions, and defined a continuum from balanced to myeloid-biased to low-output HSC states with distinct transcription factor and signalling activities [8]. Adult HSCs showed enrichment of lineage-specific transcription factor motifs whereas fetal cells lacked such enrichment and frequently generated mixed-lineage colonies, indicating that lineage preference is encoded in chromatin accessibility [13]. Whole-blood methylomes retain a long-lived cellular memory of ancestral hematopoietic progenitor states rather than only reporting current mature composition [14]. Human HSC clones maintained stable lineage biases, and although busulfan and irradiation altered the magnitude, lineage bias and timing of reconstitution without changing engraftment, HSC clones were less sensitive to conditioning than multipotent progenitor clones [17].

The myeloid set point is retuned downstream of the stem cell and by environment

Clonal expansion in progenitor compartments rather than in primitive HSCs drove myeloid and megakaryocyte output in native hematopoiesis [3]. In the early postnatal period, roughly 75% of Flt3+ hematopoietic progenitors showed unipotent fates and generated a wave of progeny lasting more than a year, indicating that lineage-restricted intermediate progenitors with long-lived output shape compartment composition [11]. Monocytes were traced to a precursor shared with lymphoid lineages and distinct from neutrophils, with classical common lymphoid progenitors redefined as lymphoid-monocyte progenitors proposed as their ancestral source [10]. Genetically identical C57BL/6 mice raised under "natural immune" conditions with pet-shop mice had more neutrophils at baseline than specific-pathogen-free mice, closer to human composition [18]. Among aged mice, those with high versus low myeloid skewing differed in inflammatory cytokines, HSC repopulation capacity and epigenetic age, and in humans a high degree of myeloid skewing was associated with higher inflammatory markers, frailty and mortality hazard [15]. Myeloid-biased HSCs marked by Neogenin-1 were already identifiable in fetal liver, and myeloid-biased programmes were progressively reinforced across the Hoxb5+ HSC compartment with age [16].

Conflicts and gaps

There is a genuine mechanistic disagreement. One set of papers attributes donor-to-donor variation in lineage output to the recipient niche, to the long-term/short-term HSC ratio, or to extrinsic feedback and competition, explicitly denying cell-intrinsic bias [1] [2] [7]; another set documents durable, clone-intrinsic states with identifiable molecular regulators [4] [8] [17]. These claims are not reconciled by any retrieved experiment, because nearly all lineage-bias measurements are made after irradiation or conditioning, which is itself the variable one camp identifies as causal [1] [17]. Second, almost no retrieved paper measures monocyte fractions specifically; the dominant readouts are myeloid-versus-lymphoid ratios or platelet/erythroid bias, and because monocytes appear to derive from a precursor shared with lymphoid rather than myeloid lineages [10], monocyte proportions may not follow the myeloid-lymphoid axis at all. Third, "stable" is undefined in duration: progressive accumulation of myeloid-biased HSCs and reinforcement of myeloid programmes with age indicate drift on the timescale over which stability is claimed [4] [16]. Fourth, three incompatible generative frameworks — chaotic dynamics, reversible-quiescence stochastic models, and feedback-competition models — each fit their own data but have not been fitted to a common dataset [6] [5] [7].

What would change the answer

A longitudinal clonal-barcoding experiment in unmanipulated, co-housed inbred mice — using Sleeping Beauty, Polylox or sequential barcoding [3] [4] — with repeated monocyte, neutrophil and lymphoid fraction measurement over many months, would establish whether clone composition predicts an individual's set point in native hematopoiesis. Splitting a single HSC pool across multiple unirradiated recipients, and repeating this after busulfan versus irradiation conditioning, would distinguish clone-intrinsic from niche-determined variance directly [1] [17]. Fitting the chaotic, two-compartment-quiescence and feedback-competition models to the same longitudinal clonal dataset, with formal model comparison, would resolve which generative mechanism is necessary [6] [5] [7]. Cross-fostering or gnotobiotic colonisation of genetically identical animals would quantify the environmental share of the set point against the stochastic share [18]. Finally, resolving monocyte output at the lymphoid-monocyte progenitor node rather than at the CMP/GMP node would test whether monocyte proportions vary independently of the myeloid-lymphoid ratio [10].

References

[1] Shamie, I., Bliss-Moreau, M., Lee, J. C., Mathieu, R., Hoffman, H. M., Geng, B., Lewis, N. E., Zhu, Y. P., Croker, B. A.. "Comparative single-cell lineage bias in human and murine hematopoietic stem cells". bioRxiv. 2024-11-19T00:00:00.000Z. URL: https://doi.org/10.1101/2024.11.19.624262

[2] Nishi, K., Sakamaki, T., Nagasaka, A., Kao, K. S., Sadaoka, K., Asano, M., Yamamoto, N., Takaori-Kondo, A., Miyanishi, M.. "Alteration of long and short-term hematopoietic stem cell ratio causes myeloid-biased hematopoiesis". bioRxiv. 2025-04-29T00:00:00.000Z. URL: https://doi.org/10.1101/2024.01.18.576239

[3] You, C., Zhang, Z., Lin, L., Sun, J.. "Clonal analysis of murine HSC self-renewal and differentiation in native hematopoiesis". bioRxiv. 2024-11-22T00:00:00.000Z. URL: https://doi.org/10.1101/2024.11.22.624575

[4] Fukushima, T., Kristiansen, T. A., Wong, L. P., Tanaka, Y., Yagi, M., Chang, Y.-H., Kimura, T., Keyes, S., Mazzola, M., Zhao, T., He, L., Goyama, S., Yamamoto, R., Hochedlinger, K., Yamazaki, S., Sadreyev, R. I., Scadden, D. T.. "Hematopoietic stem cells undergo bidirectional fate transitions in vivo". bioRxiv. 2026-07-08T00:00:00.000Z. URL: https://doi.org/10.1101/2025.02.23.639689

[5] Garcia Vilela, Y., Thielecke, L., Cesar Fassoni, A., Glauche, I.. "Long-term clonal analysis using stochastic models reveals heterogeneity and quiescence of hematopoietic stem cells". bioRxiv. 2025-09-29T00:00:00.000Z. URL: https://doi.org/10.1101/2025.09.24.677849

[6] Jia, D., Cavazos, E. S., West, T., Liang, S.-H., Costa, R., Salomon, M. C., Huang, A., Trinchieri, G., Lionakis, M., Mukherjee, R., Altan-Bonnet, G.. "Chaotic dynamics for homeostatic hematopoiesis". bioRxiv. 2025-03-10T00:00:00.000Z. URL: https://doi.org/10.1101/2024.08.16.608266

[7] Rodriguez-Correa, E., Gruenschlaeger, F., Nizharadze, T., Anstee, N., Al-Sabah, J., Kumpost, V., Sedlmeier, A., Li, C., Ball, M., Fotopoulou, F., Jayarajan, J., Ghezzi, I., Knoch, J., Druce, M., Aurich, T., Buechler-Schaeff, M., Lux, S., Hernandez-Malmierca, P., Graesel, J., Vonficht, D., Mathioudaki, A., Zaugg, J. B., Mikut, R., Trumpp, A., Hoefer, T., Huebschmann, D., Haas, S., Milsom, M. D.. "A kinetics-based model of hematopoiesis reveals extrinsic regulation of skewed lineage output from stem cells". bioRxiv. 2025-02-05T00:00:00.000Z. URL: https://doi.org/10.1101/2025.02.04.636388

[8] Fukushima, T., Nishiyama, A., Koide, S., Isobe, T., Yabushita, T., Asada, S., Goyama, S., Iwama, A., Yamazaki, S., Tamura, T., Kitamura, T., Suda, T., Tanaka, Y.. "DNA barcoding-based paired daughter cell analysis reveals division preferences of hematopoietic stem cells". bioRxiv. 2026-07-24T00:00:00.000Z. URL: https://doi.org/10.64898/2026.07.24.739718

[9] Umar, Y. J., Savvopoulos, S. V., Pitsalidis, C., Mitroulis, I., Hatzikirou, H.. "In Silico Investigation of the Role of Local and Global Inflammation-Driven Feedback in Myelopoiesis and Clonal Cell Expansion". bioRxiv. 2025-11-24T00:00:00.000Z. URL: https://doi.org/10.1101/2025.08.15.670507

[10] YUE, Y., Chen, S.. "Monocyte and Lymphoid Lineages Share a Common Precursor Distinct from Neutrophils in Hematopoiesis". bioRxiv. 2025-12-25T00:00:00.000Z. URL: https://doi.org/10.64898/2025.12.25.696474

[11] Cirovic, B., Nizharadze, T., Dietlein, N., Henrich-Kellner, C., Hoefer, T., Rodewald, H.-R.. "Early postnatal Flt3+ hematopoietic progenitors realize fate-restricted and long-lived output in vivo". bioRxiv. 2026-04-13T00:00:00.000Z. URL: https://doi.org/10.64898/2026.04.09.716798

[12] Secchi, M. A., Liu, Q., Anton, S. G., Mai, C., Jibril, A., Xu, J., O'Carroll, D., Wilson, N. K., Kranc, K. R., Gottgens, B., Lo Celso, C., Luis, T. C.. "A new double reporter strategy reveals a subset of non-migratory hematopoietic stem cells localized in a dynamic bone marrow niche.". bioRxiv. 2025-07-21T00:00:00.000Z. URL: https://doi.org/10.1101/2025.07.20.665772

[13] Palo, S., Nagaharu, K., Sommarin, M. N. E., Olofzon, R., Turati, V., Soneji, S., Karlsson, G., Böiers, C.. "Chromatin Accessibility Shapes Developmental-Specific Lineage Plasticity in Hematopoiesis". bioRxiv. 2025-12-04T00:00:00.000Z. URL: https://doi.org/10.64898/2025.12.02.691283

[14] Hukkanen, M., Nitta Fernandes, F. A., Davis, L. R., Hofer, M., Bardon, G., Paciello, E., Zahn, S., Zhu, T., Westerlund, S., Meyer, B. S., Ollikainen, M., Le Bohec, C., Cristofari, R.. "An epigenetic palimpsest: the King penguin's blood methylome reveals conserved hematopoietic ageing". bioRxiv. 2026-10-13T00:00:00.000Z. URL: https://doi.org/10.64898/2026.10.10.757848

[15] Niemann, J., Stahl, S., Sakk, V., Perez-Correa, J.-F., Wagner, W., Jaensch, A., Rothenbacher, D., Zheng, Y., Mulaw, M. A., Geiger, H., Vogel, M.. "The extent of myeloid skewing in blood is a biomarker of biological aging in mice and humans". bioRxiv. 2026-01-22T00:00:00.000Z. URL: https://doi.org/10.64898/2026.01.20.700344

[16] Banuelos, A., Baez, M., Yilmaz, L., Koren-Sedova, E., Zukowska, M., Burden, A. T., Swartzrock-Willner, L., Le, U., Zhang, A., Georgeos, N., Ohene-Gambill, B., Zheng, B., Womack-Gambrel, N., Honjol, R., Sinha, R., Weissman, I. L.. "Neogenin-1 marks myeloid-primed fetal hematopoietic stem cells that undergo progressive lineage-restriction with age". bioRxiv. 2026-09-03T00:00:00.000Z. URL: https://doi.org/10.64898/2026.09.01.747091

[17] Vergel Snodgrass, M. C., Eerdeng, J., Condie, P., Lu, R.. "Tracking Human HSCs and MPPs in Mice Reveals Distinct Clonal Dynamics and Responses to Pre-transplant Conditioning". bioRxiv. 2026-02-12T00:00:00.000Z. URL: https://doi.org/10.64898/2026.02.10.705085

[18] Byskosh, A., Pulford, J., Murzin, E., Michael, Z., Lovell, C. G., Niu, B., Seshadri, A., Brat, G., Zajdel, A., Borriello, F., Lederer, J.. "A Multigenerational ''Dirty'' Mouse Model for Studying Trauma-Induced Immune Dysregulation and Infection Susceptibility". bioRxiv. 2025-06-24T00:00:00.000Z. URL: https://doi.org/10.1101/2025.06.18.660185

[19] Ma, T., Fleischman, A. G., Wodarz, D., Komarova, N.. "Hierarchical tissue structure creates history-dependent barriers to clonal invasion". bioRxiv. 2026-08-10T00:00:00.000Z. URL: https://doi.org/10.64898/2026.08.04.742588

How this survey was made

This survey answers one question: “How do stochastic differences in hematopoietic stem and progenitor cell output generate stable, individual-specific variation in monocyte and other myeloid lineage proportions in genetically identical mammals?”

We keep our own collection of recent preprints and refresh it every day from bioRxiv, medRxiv, arXiv, PsyArXiv, SocArXiv and ChemRxiv. For each survey, we search that collection for recent work and keep the strongest matches: papers that use the same terms as the question, and papers on the same topic in different words.

The text was written from the titles and abstracts of those papers; the full texts were not read. Statements that draw on a particular paper are marked with a number in brackets. The reference list just above is the 19 papers whose work this text draws on, chosen from the 154 papers those searches returned; the rest of the shortlist is not printed. It is not a complete review of everything published on the question.

Treat this as a starting point and a reading list, not a peer-reviewed review. The searches behind this survey ran on October 21, 2026 and covered work from the previous two years.