Neutrophil progenitor cells and related methods and uses

Abstract

A method of identifying a neutrophil progenitor cell is provided, the method comprising: determining expression of at least one biomarker in a cell, the biomarker selected from the group consisting of CD71, LOX-1, CD164, CD112, CD181, TACSTD2, CD1 lb, and CD49d. In various embodiments, the cell is identified as a neutrophil progenitor cell when determined to have at least one of the following expression profiles: CD71 高 / + , LOX-1 中 / 低 / ‑ , CD164 高 / + , CD112 高 / + , CD181 中 / 低 / ‑ , TACSTD2 高 / + , CD1 lb 低 / ‑ , and / or CD49d 中 / 高 / + . Also disclosed is a method of sorting and / or isolating a neutrophil progenitor cell from a population of cells, a composition enriched in neutrophil progenitor cells, and related uses and methods.

Description

TECHNICAL FIELD

[0001] The present disclosure relates broadly to granulocyte-monocyte progenitor cell (GMP) subpopulations, such as neutrophil progenitor cells, and related methods and uses. BACKGROUND

[0002] Hematopoietic stem cell transplantation (HSCT) is a common therapy for hematological disorders and neoplastic diseases. However, the generation of neutrophils, which are important immune cells that provide protection against bacterial and fungal infections, from stem cells is a lengthy process, estimated to take 3-4 weeks in humans. As a result, neutropenia after transplantation often leads to morbidity and mortality from infections. Current clinical management, which includes administration of prophylactic antimicrobial drugs, recombinant growth factors (e.g., G-CSF), or repeated donor granulocyte infusions (DGI) in isolation rooms to provide immune protection to patients during neutrophil deficiency periods, can be ineffective and associated with high healthcare costs.

[0003] Accordingly, there is a need to address or at least ameliorate one or more of the aforementioned problems. In particular, it is necessary to provide a method of identifying a neutrophil progenitor cell, a method of sorting and / or isolating a neutrophil progenitor cell from a cell population, a composition enriched in neutrophil progenitor cells, and related uses and methods that address or at least ameliorate one or more of the aforementioned problems. SUMMARY

[0004] In one aspect, there is provided a method of identifying a neutrophil progenitor cell, the method comprising: determining expression of at least one biomarker in a cell, the biomarker selected from the group consisting of CD71, LOX-1, CD164, CD112, CD181, TACSTD2, CD11b, and CD49d; and identifying the cell as a neutrophil progenitor cell when it is determined to have at least one of the following expression profiles: CD71 高 / + , LOX-1 中 / 低 / - , CD164 高 / + , CD112 高 / + , CD181 中 / 低 / - , TACSTD2 高 / + , CD11b 低 / - , and / or CD49d 中 / 高 / + .

[0005] In one embodiment, the biomarker comprises CD71, and the cell is identified as a neutrophil progenitor cell when it is determined to have CD71 高 / + expression.

[0006] In one embodiment, when the cell is identified as a neutrophil progenitor cell, the method further comprises: determining expression of a further biomarker selected from CD49d and / or side scatter (SSC) properties of the neutrophil progenitor cell; and identifying a subtype of the neutrophil progenitor cell according to the expression of the further biomarker and / or the side scatter properties.

[0007] In one embodiment, when the neutrophil progenitor cell is determined to be CD49d 高 / + and / or SSC 低 , then the neutrophil progenitor cell is identified as an early committed neutrophil progenitor cell, and wherein when the neutrophil progenitor cell is determined to be CD49d 中 / 低 / - and / or SSC 高 , then the neutrophil progenitor cell is identified as an intermediate neutrophil progenitor cell, which is downstream of the early committed neutrophil progenitor cell in the neutrophil lineage.

[0008] In one embodiment, determining expression of the at least one biomarker and / or the further biomarker comprises contacting the cell with one or more antibodies directed against the biomarker and / or the further biomarker.

[0009] In one aspect, there is provided a method of sorting and / or isolating a neutrophil progenitor cell from a population of cells, the method comprising: selecting a cell having at least one of the following expression profiles: CD71 高 / + , LOX-1 中 / 低 / - , CD164 高 / + , CD112 高 / + , CD181 中 / 低 / - , TACSTD2 高 / + , CD11b 低 / - and / or CD49d 中 / 高 / + .

[0010] In one embodiment, the method comprises selecting a CD71 高 / + cell.

[0011] In one embodiment, the population of cells is derived from umbilical cord blood and / or bone marrow.

[0012] In one embodiment, the method further comprises culturing the neutrophil progenitor cell to obtain proliferation and / or differentiation of the neutrophil progenitor cell, thereby obtaining progeny thereof.

[0013] In one embodiment, the method further comprises administering the neutrophil progenitor cell and / or progeny thereof to a subject in need thereof.

[0014] In one embodiment, the subject has neutropenia.

[0015] In one embodiment, the selecting step comprises contacting the cells with one or more antibodies directed against one or more of CD71, LOX-1, CD164, CD112, CD181, TACSTD2, CD11b, and CD49d.

[0016] In one aspect, there is provided a composition enriched in neutrophil progenitor cells having at least one of the following expression profiles: CD71 高 / + , LOX-1 中 / 低 / - , CD164 高 / + , CD112 高 / + , CD181 中 / 低 / - , TACSTD2 高 / + , CD11b 低 / - , and / or CD49d 中 / 高 / + .

[0017] In one embodiment, the composition is enriched in CD71 高 / + neutrophil progenitor cells.

[0018] In one aspect, there is provided the composition for use in therapy.

[0019] In one aspect, there is provided the composition for use in the treatment of neutropenia.

[0020] In one aspect, there is provided the use of the composition in the manufacture of a medicament for the treatment of neutropenia.

[0021] In one aspect, there is provided a method of preparing an infusion composition, the method comprising: enriching a composition in neutrophil progenitor cells having at least one of the following expression profiles: CD71 高 / + , LOX-1 中 / 低 / - , CD164 高 / + , CD112 高 / + , CD181 中 / 低 / - , TACSTD2 高 / + , CD11b 低 / - , and / or CD49d 中 / 高 / + .

[0022] In one embodiment, the method comprises enriching the composition in CD71 高 / + neutrophil progenitor cells.

[0023] In one embodiment, the selecting step comprises contacting the cells with one or more antibodies directed against one or more of CD71, LOX-1, CD164, CD112, CD181, TACSTD2, CD11b, and CD49d.

[0024] Definitions

[0025] The terms "treatment," "treat," and "therapy," and synonyms thereof, as used herein, refer to both therapeutic treatment and prophylactic or preventative measures, wherein the object is to prevent or slow down (lessen) an undesired medical condition, including but not limited to a disease, a symptom, and a disorder. The medical condition also includes a body's response to a disease or disorder, such as inflammation. Those in need of such treatment include those already with the medical condition as well as those in which the medical condition is to be prevented or preexisting conditions in which the medical condition is to be prevented.

[0026] As used herein, the term "therapeutically effective amount" of a compound will be that amount of active agent that is capable of preventing or at least slowing down (lessening) a medical condition, such as neutropenia. Dosage and administration of the compounds, compositions, and formulations of the present disclosure can be determined by one of ordinary skill in the clinical pharmacology or pharmacokinetics. See, e.g., Mordenti and Rescigno, (1992) Pharmaceutical Research. 9: 17-25; Morenti et al., (1991) Pharmaceutical Research. 8: 1351-1359; and Mordenti and Chappell, "The use of interspecies scaling in toxicokinetics" in Toxicokinetics and New Drug Development, Yacobi et al. (eds.) (Pergamon Press: NY, 1989), pp. 42-96. The effective amount of the active agents of the present disclosure used therapeutically will depend, e.g., on the therapeutic objectives, the route of administration, and the condition of the patient. Accordingly, it can be necessary to adjust the dose and alter the route of administration as required by the therapeutic objectives.

[0027] The term "subject," as used herein, includes patients and non-patients. The term "patient" refers to an individual who has or is likely to have a certain medical condition, such as cancer, while a "non-patient" refers to an individual who does not have or is not likely to have the medical condition. "Non-patients" include healthy individuals, non-diseased individuals, and / or individuals free of the medical condition. The term "subject" includes humans and animals. Animals include murine and similar animals. "Murine" refers to any mammal from the Muridae family, such as mice, rats, and the like.

[0028] As used herein, "expression" refers to both genotypic expression and phenotypic expression of a biomarker in the present disclosure.

[0029] A "biomarker" refers to a molecule such as a protein, carbohydrate structure, glycolipid, glycoprotein (including cell surface glycoprotein), receptor (including cell surface receptor), or gene (or nucleic acid encoding the gene) whose expression in or on a cell (sample) derived from a subject (e.g., mammalian tissue) can be detected by standard methods in the art (as well as those methods disclosed herein). In some examples, a biomarker can be any molecule that can serve as an identifier (i.e., marker) of a target of interest. In some examples, a biomarker can be a cell surface receptor, cell surface glycoprotein, transcription factor, etc. In some examples, a biomarker can be used to sort, identify, detect, isolate, purify, enrich, select, sort, and / or separate a cell / cell population, and / or can be used to determine a differentiation stage, developmental stage, and / or activity state of a cell.

[0030] In some examples, where a biomarker comprises a cell surface marker, expression of the marker can be represented in accordance with accepted nomenclature known in the art. For example, for the cell surface marker CD71, CD71 + refers to a cell that positively expresses CD71, CD71 - refers to a cell that does not express detectable CD71, CD71 低 refers to a cell that expresses low CD71, CD71 中 refers to a cell that expresses medium CD71, and CD71 高 refers to a cell that expresses high CD71.

[0031] As used in the specification herein, an agent for detecting a biomarker in the present disclosure refers to any compound, molecule, and / or system that functions to detect the presence / absence of a biomarker and / or its expression or level in the present disclosure. Such an agent is capable of directly or indirectly detecting and / or binding to a biomarker. In the present disclosure, additional moieties can be required to enhance detection of a biomarker, for example, by / through amplified optical diffraction. Examples of agents and additional moieties include, but are not limited to, proteins (e.g., antigen-binding proteins such as antibodies or fragments thereof, enzymes such as horseradish peroxidase and alkaline phosphatase, etc.), polynucleotides (e.g., aptamers), and small molecules (e.g., metal nanoparticles).

[0032] The term "micron-scale" as used herein should be interpreted broadly to include sizes of about 1 micron to about 1000 microns.

[0033] The term "nanoscale" as used herein should be interpreted broadly to include sizes of less than about 1000 nm.

[0034] The term "particle" as used herein refers broadly to a discrete entity or discrete body. The particles described herein can include organic, inorganic, or biological particles. The particles used and described herein can also be a macro-particle, which is aggregated from a plurality of sub-particles or fragments of small objects. The particles of the present disclosure can be spherical, substantially spherical, or non-spherical particles, such as irregularly shaped particles or ellipsoidal particles. The term "size" when used in reference to a particle refers broadly to the largest dimension of the particle. For example, when the particle is substantially spherical, the term "size" can refer to the diameter of the particle; or when the particle is substantially non-spherical, the term "size" can refer to the largest length of the particle.

[0035] The terms "coupled" or "connected," as used in the present specification, are intended to encompass a direct connection between two elements and / or an indirect connection between two elements through one or more intermediate elements.

[0036] The term "associated with," as used herein when referring to two elements, refers to a broad relationship between the two elements. The relationship includes, but is not limited to, a physical, chemical, or biological relationship. For example, when element A is associated with element B, element A and B can be directly or indirectly attached to one another, or element A can comprise element B, and vice versa.

[0037] The term "adjacent to," as used herein when referring to two elements, refers to one element being in close proximity to the other element, and can be, but is not limited to, elements that are in contact with one another, or can further include that the elements are separated by one or more additional elements disposed between the elements.

[0038] The term "and / or", such as "X and / or Y" is understood to mean "X and Y" or "X and Y", and is to be taken as explicit support for both meanings or either meaning.

[0039] Also, in the description herein, whenever used, the word "substantially" is understood to include, but not be limited to "entirely" or "completely" or the like. In addition, whenever used, terms such as "comprising," "comprise" and the like are intended to be descriptive language and not limiting. That is, they are intended to mean that the embodiments disclosed contain the recited elements or steps but not excluding others. For example, when "comprising" is used, it is intended that the mention of "a" or "one" does not exclude the presence of more than one, unless otherwise indicated. Terms such as "consisting" or "consist" are to be considered subsets of the terms such as "comprising" or "comprise." Thus, in embodiments disclosed herein using terms such as "comprising," "comprise" and the like, it is understood that the embodiments provide teaching of corresponding embodiments using terms such as "consisting" or "consist." Also, whenever used, terms such as "about," "approximately" and the like are generally intended to mean a reasonable variation, for example + / - 5% of a disclosed value, or + / - 4% of a disclosed value, or + / - 3% of a disclosed value, or + / - 2% of a disclosed value, or + / - 1% of a disclosed value.

[0040] Also, in the description herein, certain values can be disclosed in ranges. Values shown as endpoints of ranges are meant to be "open" - i.e., not a limitation to that specific value. Whenever a range is described, it is meant to include all possible sub-ranges as well as the individual numerical values within that range. That is, every numerical value within the range is also explicitly disclosed. For example, a range of 1% to 5% is explicitly disclosed as a range of 1% to 2%, 1% to 3%, 1% to 4%, 2% to 3%, etc., as well as the individual values 1%, 2%, 3%, 4%, and 5%. It is understood that the individual numerical values are also explicitly disclosed in the range. Also, whenever a range is described, it is meant to include the endpoints of the range as well as values that are approximately the endpoints of the range. For example, a range of 1% to 5% is explicitly disclosed as a range of 1.00% to 5.00%, as well as 1.0% to 5.0%, and all values within these ranges (e.g., 1.01%, 1.02%,... 4.98%, 4.99%, 5.00%, and 1.1%, 1.2%,... 4.8%, 4.9%, 5.0%, etc.). The above-described intent is applicable to ranges of any magnitude.

[0041] Additionally, in describing some embodiments, the disclosure can present methods and / or processes as a particular sequence of steps. However, unless otherwise specified, the method or process should not be limited to the specific sequence of steps disclosed. Other sequences of steps can also be possible. The specific sequences of steps disclosed herein should not be understood as overly limiting. Unless otherwise specified, the methods and / or processes disclosed herein should not be limited to steps performed in written order. The sequence of steps can vary and still be within the scope of the disclosure.

[0042] Also, it should be understood that, although the disclosure provides embodiments having one or more features / tendencies discussed herein, one or more of these features / tendencies can be eliminated in other alternative embodiments, and the disclosure provides support for such elimination and these related alternative embodiments.

[0043] Description of Embodiments

[0044] Exemplary, non-limiting embodiments of methods of characterizing / identifying granulocyte-monocyte progenitor cell (GMP) populations, such as neutrophil progenitor cell subpopulations, and related compositions, methods, and kits are disclosed below.

[0045] GMPs are known to give rise to granulocytes (e.g., neutrophils, eosinophils, and basophils) and monocytes. In some examples, GMPs are found to comprise a mixed population of monocyte, neutrophil, eosinophil, and basophil progenitor subsets. In various embodiments, a method of classifying, identifying, detecting, isolating, purifying, enriching, selecting, sorting, and / or separating GMP cells / populations / subpopulations / subsets, and / or determining the differentiation stage, developmental stage, and / or activity state of the cells / populations / subpopulations / subsets is provided. GMP subpopulations (or subsets) can include monocyte progenitor cell subpopulations, neutrophil progenitor cell subpopulations, eosinophil progenitor cell subpopulations, and basophil progenitor cell subpopulations. Other distinct subpopulations (or subsets) can also exist within these progenitor cell subpopulations.

[0046] In various embodiments, the method comprises determining the transcriptome profile and / or proteome profile of the cells / populations / subpopulations / subsets. In various embodiments, the method comprises determining and / or measuring the presence / absence / amount / level / proportion of one or more markers / biomarkers / signatures in the cells / populations / subpopulations / subsets. In various embodiments, the method comprises determining and / or measuring one or more physical properties of the cells / populations / subpopulations / subsets. The determination or measurement can be quantitative, semi-quantitative, or qualitative.

[0047] In various embodiments, the method comprises determining or measuring the presence / absence / amount / level / proportion of at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, or at least about 10 markers / biomarkers and / or physical properties associated with the cell / population / subpopulation / subset. In various embodiments, the method comprises determining or measuring the presence / absence / amount / level / proportion of no more than about 10, no more than about 9, no more than about 8, no more than about 7, no more than about 6, no more than about 5, no more than about 4, no more than about 3, no more than about 2, or no more than about 1 marker / biomarker and / or physical property associated with the cell / population / subpopulation / subset.

[0048] Markers / biomarkers / signatures, or components thereof, include, but are not limited to, polypeptides (e.g., cell surface proteins) and polynucleotides (e.g., DNA and RNA). Markers / biomarkers / signatures can be transcriptomic and / or proteomic markers / biomarkers / signatures. In some embodiments, markers comprise RNA markers / biomarkers / signatures. In some embodiments, markers comprise protein markers / biomarkers / signatures. In some embodiments, markers comprise surface / cell surface markers / biomarkers / signatures. In some embodiments, markers comprise transcription factors.

[0049] In various embodiments, the marker / biomarker / signature is selected from the group of 33D1, 4-1BB ligand, APCDD1, B220, B7H4, B7-H4, C3aR, Cadherin 11, CCR10, CCRL2, CCX-CKR (CCRL1), CD10, CD100, CD102, CD103, CD104, CD105 (Endoglin), CD106, CD107a, CD107b, CD109, CD111, CD112, CD114, CD115, CD116, CD117, CD119, CD11a, CD11b, CD11c, CD120a, CD120b, CD121a, CD122, CD123, CD124, CD126, CD127, CD129, CD13, CD130, CD131, CD132, CD133, CD134, CD135, CD137, CD138, CD14, CD140a, CD140b, CD141, CD142, CD143, CD144, CD146, CD147, CD148, CD15, CD150, CD151, CD152, CD152 (CTLA-4), CD153, CD154, CD155, CD156c, CD157, CD158, CD158b, CD158e1, CD159a, CD16, CD16.2, CD160, CD161, CD162, CD163, CD164, CD165, CD166, CD169, CD170, CD172a (SIRPa), CD172b (SIRPb), CD172g (SIRPy), CD178, CD179a, CD179b, CD18, CD180, CD181, CD182, CD183, CD184, CD185, CD186, CD186 (CXCR6), CD19, CD191, CD192, CD193, CD194, CD195, CD196, CD197, CD198, CD199, CD1a, CD1b, CD1c, CD1d, CD2, CD20, CD200, CD200R, CD200R3, CD201, CD202b, CD203c, CD205, CD206, CD207, CD209, CD21, CD210, CD213a1, CD213a2, CD215, CD217, CD218a, CD22, CD220, CD220R, CD221, CD223 (LAG-3), CD226, CD227, CD229, CD23, CD230 (Prion), CD235ab, CD24, CD243, CD244 (2B4), CD245, CD25, CD252, CD253, CD254, CD255, CD258, CD26, CD261, CD262, CD263, CD265, CD266, CD267, CD268, CD269, CD27, CD272, CD273, CD274, CD275, CD276, CD277, CD278, CD279, CD28, CD282, CD284, CD29, CD290, CD294, CD298, CD3, CD30, CD300c, CD300d, CD300LG, CD301, CD301b, CD303, CD304, CD307e, CD309, CD31, CD314, CD317, CD318, CD319, CD32, CD323, CD324, CD325, CD326, CD328, CD33, CD334, CD335, CD336, CD337, CD338, CD339, CD34, CD34_MEC14.7, CD34_SA376A4, CD340, CD344, CD35, CD351, CD352, CD354, CD355, CD357, CD36, CD360, CD365, CD366, CD368, CD369, CD36L1, CD37, CD370, CD371, CD38, CD39, CD3e, CD4, CD40, CD41, CD42b, CD43, CD44, CD45, CD45.1, CD45.2, CD45RA, CD45RB, CD45RO, CD46, CD47, CD48, CD49a, CD49b, CD49c, CD49d, CD49e, CD49f, CD5, CD50, CD51, CD52, CD54, CD55, CD56 (NCAM), CD57, CD58, CD59, CD59a, CD6, CD61, CD62L, CD62P, CD63, CD64, CD66a, CD66b, CD66c, CD66e, CD69, CD7, CD70, CD71, CD73, CD74, CD79b, CD80, CD81, CD82, CD83, CD84, CD85, CD85d, CD85h, CD85k, CD86, CD87, CD88, CD89, CD8a, CD8b, CD9, CD90, CD90.1, CD90.2, CD92, CD93, CD94, CD95, CD96, CD97, CD98, CD99, cKit, CLEC4A, CX3CR1, CXCL16, CXCR7, DcTRAILR1, delta opioid receptor, delta like 1 (DLL1), delta like 4 (DLL4), dopamine receptor D1 (DRD1), DR3, E-cadherin, EGFR, EphA2, erbB3, ESAM, F4 / 80, Fc epsilon Rl alpha, FLT3, FPR3, FR4, Galectin 9, Ganglioside GD2, GARP, GITR ligand, GL7, GPR19, GPR56, GPR83, Gr1, H2, HLA-A, HLA-B, HLA-C, HLA-DR, HLA-E, HVEM, IA / IE, IFNAR1, IFNgR b chain, IFN-gamma Rb chain, Ig kappa light chain, Ig lambda light chain, IgD, IgG Fc, IgM, IL21R, IL23R, IL-28RA, IL33R, IL33Ra, Integrin alpha9beta1, Integrin beta5, Integrin beta7, Isotype AH IgG, Isotype mlgGl, Isotype mlgG2a, Isotype mlgG2b, Isotype mlgM, Isotype rlgGl, Isotype rlgG2a, Isotype rlgG2b, Isotype rlgG2c, Isotype rlgM, Isotype SH IgG, Jagged 2, JAML, KLRG1, Ksp37, LAP, LOX-1, LPAM_1, Ly-49A, Ly108, Ly49CFIH, Ly49D, Ly49H, Ly51, Ly6C, Ly6D, Ly6G, LY6G6D, Ly6K, Lymphotoxin beta receptor, Mac2, Mac3, MAdCAM1, MAIR-IV, MAIRV, MD1, MERTK, MICA, MICB, MRGX2, MSC, MUC-13, NK1.1, NKG2D, NKp80, Notch 1, Notch 2, Notch 3, Notch 4, Notch3, NPC, PD1H, PDC-TREM, PIR A, PIR B, Plexin B2, Podoplanin, PSMA, RAE1y, Sca-1, Sialyl Lewis X (dimer), Siglec H, Siglec-10, Siglec-8, Siglec-9, SiglecF, SiglecH, SSC, SSEA-1, SSEA-3, SSEA-4, SSEA-5, SUSD2, TACSTD2, TCR gd, TCR Vd1.1_1.2, TCRa, TCR b, TCRb chain, TER119, TIGIT (VSTM3), Tim-2, Tim-4, TLR4, TLT-2, TM4SF20, TMEM8A, TNAP, TRA-1-60-R, TRA-1-81, TRA-2-49, TRA-2-54, Trem-like 4, TSLPR, VEGFR-3, VISTA, and XCR1. In various embodiments, the marker / biomarker / signature includes a marker / biomarker / signature discussed in the Examples section of the present disclosure.

[0050] In some examples, an early neutrophil progenitor population is found in the GMP that extends along a developmental trajectory toward mature neutrophils. Accordingly, in various embodiments, a method of classifying, identifying, detecting, isolating, purifying, enriching, selecting, sorting, and / or separating a neutrophil progenitor / neutrophil progenitor population, and / or determining a differentiation stage, developmental stage, and / or activity state of a neutrophil progenitor / neutrophil progenitor population is provided. In various embodiments, the neutrophil progenitor includes a committed neutrophil progenitor. For example, under physiological conditions, the neutrophil progenitor can be unable to differentiate into a cell of a non-neutrophil lineage, such as a monocyte. In some examples, when the neutrophil progenitor is cultured with a factor that promotes differentiation toward a monocyte lineage (e.g., CSF-1), the neutrophil progenitor produces Ly6G + neutrophil, rather than a macrophage. Accordingly, although the neutrophil progenitor can differentiate along the neutrophil lineage (in a specialized manner) (e.g., to produce a preNeu, immature neutrophil, or mature neutrophil), the neutrophil progenitor can not differentiate into a cell of a non-neutrophil lineage. In various embodiments, the neutrophil progenitor is capable of producing a mature neutrophil, such as a CD16 + CD10 + mature neutrophil. In various embodiments, the neutrophil progenitor includes an early neutrophil progenitor (e.g., a neutrophil progenitor that is upstream of preNeus and other neutrophil precursors in the neutrophil lineage). In various embodiments, the neutrophil progenitor has a high proliferative capacity (e.g., a higher proliferative capacity than preNeus and other neutrophil precursors).

[0051] The neutrophil progenitor cells can be animal or human neutrophil progenitor cells. In some embodiments, the neutrophil progenitor cells include mammalian (e.g., human, non-human primate, canine, murine (e.g., mouse, rat, rabbit, etc.), and the like) neutrophil progenitor cells. In some embodiments, the neutrophil progenitor cells include mouse neutrophil progenitor cells. In some embodiments, the neutrophil progenitor cells include human neutrophil progenitor cells.

[0052] In various embodiments, a method of identifying a neutrophil progenitor cell is provided, the method comprising: determining expression of at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, or at least about 6, at least about 7, at least about 8, at least about 9, at least about 10, at least about 11, at least about 12, at least about 13, at least about 14, or at least about 15 biomarkers in the cell, the biomarkers selected from the group consisting of CD71, LOX-1, CD164, CD112, CD181, TACSTD2, Ly6C, CD115, CD1 lb, CD34, CD81, CD49a, CD49d, CD106, and CD63. In various embodiments, a method of identifying a neutrophil progenitor cell is provided, the method comprising: determining expression of at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, or at least about 8 biomarkers in the cell, the biomarkers selected from the group consisting of CD71 (transferrin receptor protein 1), LOX-1 (lectin-like oxidized low-density lipoprotein (LDL) receptor-1), CD164 (sialomucin core protein 24 or endolyn or cluster of differentiation 164), CD112 (poliovirus receptor-related protein 2 (PVRL2) or nectin-2), CD181 (interleukin 8 receptor alpha or cluster of differentiation 181), TACSTD2 (tumor-associated calcium signal transducer 2), CD1 lb (integrin alpha M or cluster of differentiation molecule 1 lb), and CD49d (integrin alpha 4). The cell can be a GMP cell (e.g., a cell having one or more of the following expression profiles: Lin - , cKit + , Sca-1 - , CD34 高 / + , and CD16 / 32 高 ), a bone marrow cell (e.g., a fetal bone marrow cell), or a cord blood cell. The cell can be an animal cell or a human cell. In various embodiments, the cell is determined to be a CD71 高 / + , LOX-1 中 / 低 / - , CD164 高 / + , CD112 高 / +CD181 中 / 低 / - TACSTD2 高 / + CD11b 低 / - and / or CD49d 中 / 高 / + If so, then the cell is identified as a neutrophil progenitor cell. Accordingly, in various embodiments, the method comprises: determining expression of at least one biomarker in the cell, the biomarker selected from the group consisting of CD71, LOX-1, CD164, CD112, CD181, TACSTD2, CD11b, and CD49d; and identifying the cell as a neutrophil progenitor cell when the cell is determined to have at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, or at least about 8 of the following expression profiles: CD71 高 / + LOX-1 中 / 低 / - CD164 高 / + CD112 高 / + CD181 中 / 低 / - TACSTD2 高 / + CD11b 低 / - and / or CD49d 中 / 高 / + Advantageously, it was discovered that the biomarkers disclosed herein are capable of distinguishing neutrophil progenitor cells from other types of cells or heterogeneous cell populations (e.g., GMP populations). In other words, neutrophil progenitor cells can be characterized or identified by expression of one or more of these biomarkers.

[0053] In one embodiment, the biomarker comprises CD71. Accordingly, in one embodiment, the cell is identified as a neutrophil progenitor cell when the cell is determined to express CD71 or when the cell is determined to be CD71 hi / + Accordingly, in various embodiments, a method of identifying a neutrophil progenitor cell is provided, the method comprising: determining expression of CD71 in the cell; and identifying the cell as a neutrophil progenitor cell when the cell is determined to express CD71. In some examples, CD71 was found to be exclusively expressed by neutrophil progenitor cells among total cord blood cells.

[0054] In some embodiments, the method can comprise determining expression of a transcription factor in the cell. In some embodiments, the method can comprise determining expression of at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, or at least about 7 transcription factors selected from the group consisting of Jagl, Sox13, Gfll, Per3, Cebpe, Etsl, and Gatal.

[0055] In neutrophil progenitor cells (referred to as proNeus), the inventors discovered two phenotypically distinct neutrophil progenitor cells (referred to as proNeul and proNeu2). In various examples, proNeul was found to have higher self-renewal potential / properties and / or stronger colony forming potential than proNeu2. In various examples, transcriptomic pathway analysis supported a decreased progenitor function for proNeu2, showing a specific enrichment in neutrophil effector functions, while proNeul was enriched in cellular components and cell survival. In various examples, proNeul numbers increased while proNeu2 remained largely unchanged during infection in a sepsis / inflammation model. In various examples, proNeu2 was shown to be a bridge between proNeul and preNeus. In various examples, proNeul was shown to generate proNeu2 and subsequent neutrophil subsets, and proNeu2 was shown to specifically generate preNeus and immature Neus. In various examples, proNeul was shown to exclusively generate downstream neutrophil subsets through C / EBPepsilon-dependent proNeu2 development. In various examples, proNeul was shown to be an early committed neutrophil, and proNeu2 was shown to be an intermediate neutrophil progenitor cell that is downstream of proNeul in the neutrophil lineage.

[0056] Thus, in various embodiments, when a cell is identified as a neutrophil progenitor cell, the method further comprises determining a subtype of the neutrophil progenitor cell. Determining a subtype of the neutrophil progenitor cell can comprise determining / measuring expression of one or more additional biomarkers in the cell and / or one or more physical properties associated with the cell.

[0057] In various embodiments, determining / measuring expression of one or more additional biomarkers in the cell comprises determining at least about 1, at least about 2, at least about 3, or at least about 4 biomarkers in the cell selected from the group consisting of: CD49d, CD34, CD106, CDl lb. In various embodiments, when the cell is determined to have one or more of the following expression profiles, then the cell is identified as an early committed neutrophil: CD49d 高 / + , CD34 高 / + , CD106 低 / - , and CDl lb 低 / - . In various embodiments, when the cell is determined to have one or more of the following expression profiles, then the cell is identified as an intermediate neutrophil progenitor cell that is downstream of an early committed neutrophil progenitor cell in the neutrophil lineage: CD49d 中 / 低 / - , CD34 低 / - , CD106高 / + and CD11b 高 / + .

[0058] In various embodiments, determining / measuring one or more physical properties associated with the cell comprises determining / measuring granularity or complexity of the cell and / or nuclear morphology of the cell. In various embodiments, when it is determined that the cell has no or minimal nuclear vacuolation falling below a threshold diameter, then the cell is identified as an early committed neutrophil. In various embodiments, when it is determined that the cell has nuclear vacuolation corresponding to or exceeding a threshold diameter, then the cell is identified as an intermediate neutrophil progenitor cell, which is downstream of the early committed neutrophil progenitor cell in the neutrophil lineage. In various embodiments, when it is determined that the cell has low granularity or complexity, then the cell is identified as an early committed neutrophil. In various embodiments, when it is determined that the cell has high granularity or complexity, then the cell is identified as an intermediate neutrophil progenitor cell, which is downstream of the early committed neutrophil progenitor cell in the neutrophil lineage. Granularity or complexity can be determined / measured by, for example, evaluating side scatter properties in flow cytometry. Thus, in various embodiments, when it is determined that the cell has low side scatter, then the cell is identified as an early committed neutrophil. Thus, in various embodiments, when it is determined that the cell has high side scatter, then the cell is identified as an intermediate neutrophil progenitor cell, which is downstream of the early committed neutrophil progenitor cell in the neutrophil lineage.

[0059] In some embodiments, when the cell is identified as a neutrophil progenitor cell, the method further comprises determining expression of another biomarker selected from CD49d and / or side scatter properties of the neutrophil progenitor cell; and identifying a subtype of the neutrophil progenitor cell based on the expression of the other biomarker and / or the side scatter properties. In various embodiments, when the cell is determined to have CD49d 高 / + expression, then the cell is identified as an early committed neutrophil progenitor cell. In various embodiments, when the cell is determined to have CD49d 中 / 低 / - expression, then the cell is identified as an intermediate neutrophil progenitor cell, which is downstream of the early committed neutrophil progenitor cell in the neutrophil lineage. In various embodiments, when the cell is determined to have low side scatter, then the cell is identified as an early committed neutrophil progenitor cell. In various embodiments, when the cell is determined to have high side scatter, then the cell is identified as an intermediate neutrophil progenitor cell, which is downstream of the early committed neutrophil progenitor cell in the neutrophil lineage. Thus, in various embodiments, when the neutrophil progenitor cell is determined to be CD49d 高 / + and / or SSC低 then the neutrophil progenitor cell is identified as an early committed neutrophil progenitor cell, and wherein when the neutrophil progenitor cell is determined to be CD49d 中 / 低 / - and / or SSC 高 then the neutrophil progenitor cell is identified as an intermediate neutrophil progenitor cell, which is downstream of the early committed neutrophil progenitor cell in the neutrophil lineage. Embodiments of the method can also be used to determine the stage of differentiation and / or development of a neutrophil progenitor cell. For example, a neutrophil progenitor cell determined to be CD49d 高 / + and / or SSC 低 may be identified as being at an earlier stage of differentiation and / or development than a neutrophil progenitor cell determined to be CD49d 中 / 低 / - and / or SSC 高 .

[0060] The expression of a biomarker can be determined or measured by methods known in the art. In some embodiments, determining / measuring the expression of a biomarker in a cell comprises performing RNA sequencing, e.g., single cell RNA sequencing. In some embodiments, determining / measuring the expression of a biomarker in a cell comprises contacting / incubating the cell with an agent for detecting the biomarker, e.g., a molecule capable of binding to or having affinity for the biomarker. Examples of agents / molecules that can be used include, but are not limited to, small molecules, peptides, proteins, antigen binding proteins, antibodies, polynucleotides, aptamers, fragments thereof, and the like. In some embodiments, determining / measuring the expression of a biomarker in a cell comprises contacting / incubating the cell with at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, or at least about 10 agents / molecules. In some embodiments, determining / measuring the expression of a biomarker in a cell comprises contacting / incubating the cell with no more than about 10, no more than about 9, no more than about 8, no more than about 7, no more than about 6, no more than about 5, no more than about 4, no more than about 3, no more than about 2, or no more than about 1 agent / molecule.

[0061] In various embodiments, the molecule comprises an antigen binding molecule. Examples of antigen binding molecules include, but are not limited to, antibodies and fragments thereof, such as antigen binding fragments. Non-limiting examples of antigen binding fragments include one or more fragments or portions of an antibody that retain the ability to specifically bind to an antigen (e.g., CD71), or synthetic modifications of antibody fragments that retain the desired binding ability to an antigen. In various embodiments, the antigen binding fragment comprises a single domain antibody, a further engineered molecule such as, but not limited to, a diabody, a triabody, a tetrabody, a minibody, and the like, a Fab fragment, a Fab' fragment, a F(ab')2 fragment, a Fd fragment, a Fv fragment, a single-chain Fv (scFv) molecule, a seFv molecule, a scFv dimer, a BsFv molecule, a dsFv molecule, a (dsFv)2 molecule, a dsFv-dsFv' molecule, a Fv fragment, a dAb fragment, a bispecific antibody, a ds diabody, a nanobody, a domain antibody, a bivalent domain antibody, and a minimal recognition unit consisting of the amino acid residues that mimic the hypervariable regions of antibodies (e.g., an isolated complementarity determining region (CDR)).

[0062] In various embodiments, determining expression of a biomarker comprises contacting the cell with one or more antibodies directed to the biomarker. In various embodiments, the antibody is coupled or conjugated with a label, e.g., a fluorescent label, such as a fluorescent dye. In various embodiments, flow cytometry is performed to determine expression of the biomarker.

[0063] In various embodiments, a method of sorting and / or isolating a neutrophil progenitor cell from a population of cells, or a method of enriching a neutrophil progenitor cell in a population of cells is provided, the method comprising: selecting a cell having / expressing one or more biomarkers as described herein. In various embodiments, a method of sorting and / or isolating a neutrophil progenitor cell from a population of cells is provided, the method comprising: selecting a cell having at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, or at least about 8 of the following expression profiles: CD71 高 / + , LOX-1 中 / 低 / - , CD164 高 / + , CD112 高 / + , CD181 中 / 低 / - , TACSTD2 高 / + , CD11b 低 / - , and / or CD49d 中 / 高 / + . In some embodiments, the method comprises selecting a cell expressing CD71 or a CD71 高 / + cell.

[0064] In various embodiments, the cell population is derived from umbilical cord blood and / or bone marrow. In various embodiments, the cell population is derived from fetal bone marrow. Thus, in various embodiments, the cell population comprises or consists of umbilical cord blood cells, bone marrow cells, and / or fetal bone marrow cells.

[0065] In various embodiments, the umbilical cord blood and / or bone marrow is collected from a mammal, such as, but not limited to, a human, a non-human primate, a canine, a murine (e.g., a mouse, a rat, a rabbit, etc.), and the like. In some embodiments, the umbilical cord blood and / or bone marrow is collected from a mouse or a human. Thus, in some embodiments, the cell population comprises at least one selected from the group consisting of mouse umbilical cord blood cells, mouse bone marrow cells, mouse fetal mouse bone marrow cells, human umbilical cord blood cells, human bone marrow cells, and human fetal bone marrow cells. In some embodiments, the cell population consists of mouse umbilical cord blood cells, mouse bone marrow cells, mouse fetal mouse bone marrow cells, human umbilical cord blood cells, human bone marrow cells, and / or human fetal bone marrow cells. In various examples, a method of isolating a subset of neutrophil progenitor cells (proNeu) from a human biological sample, such as human umbilical cord blood and fetal bone marrow, is provided, comprising using CD71 as a selection marker.

[0066] In various embodiments, the method further comprises culturing the neutrophil progenitor cells to obtain proliferation and / or differentiation of the neutrophil progenitor cells, e.g., to mature neutrophils. In various embodiments, the mature neutrophils comprise CD16 + CD10 + Mature neutrophils.

[0067] In various embodiments, the method further comprises administering the neutrophil progenitor cells and / or progeny thereof (e.g., progeny resulting from proliferation of the neutrophil progenitor cells and / or differentiation of the neutrophil progenitor cells) to a subject in need thereof. Examples of subjects in need thereof include, but are not limited to, neutropenic subjects, subjects with cancer, infected subjects (e.g., bacterial and / or fungal infection), subjects undergoing chemotherapy and / or radiation therapy, subjects with blood disorders, subjects with bone marrow disorders, subjects with immune system disorders, subjects who are candidates for hematopoietic stem cell transplantation, subjects who are candidates for bone marrow ablative therapy, and subjects having one or more of the following conditions: multiple myeloma, leukemia, lymphoma, aplastic anemia, thalassemia, sickle cell disease, severe combined immunodeficiency syndrome, and the like. In one embodiment, the subject has neutropenia.

[0068] In some embodiments, the cell population (i.e., the cell population from which the neutrophil progenitor cells are isolated or the cell population in which the neutrophil progenitor cells are enriched) is obtained / is derived from a subject. Thus, in some embodiments, the method further comprises the step of obtaining the cell population from a subject, e.g., prior to a procedure such as hematopoietic stem cell transplantation or bone marrow ablative therapy. In some embodiments, the cell population is obtained / is derived from a donor.

[0069] In various embodiments, the selecting step comprises contacting / incubating the cells with an agent for detecting the biomarker(s), e.g., a molecule(s) that is / are capable of binding to or having an affinity for the biomarker(s) as described herein. In some embodiments, the selecting step comprises contacting / incubating the cells with at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, or at least about 10 agents / molecules. In some embodiments, the selecting step comprises contacting / incubating the cells with no more than about 10, no more than about 9, no more than about 8, no more than about 7, no more than about 6, no more than about 5, no more than about 4, no more than about 3, no more than about 2, or no more than about 1 agent / molecule. In some embodiments, the molecule comprises an antibody. In various embodiments, the selecting step comprises contacting the cells with one or more antibodies directed to one or more of CD71, LOX-1, CD164, CD112, CD181, TACSTD2, CD1 lb, and / or CD49d. The antibody can be coupled or conjugated to a label, e.g., a fluorescent label, such as a fluorescent dye. In various embodiments, flow cytometry is performed to select the neutrophil progenitor cells. In various embodiments, fluorescence-activated cell sorting (FACS) is used to select the neutrophil progenitor cells.

[0070] In various embodiments, the method further comprises washing the cells. In various embodiments, the method further comprises removing the antibody bound to the biomarker. For example, the REAlease releasable antibody technology (Miltenyl Biotech) can be utilized. A REAlease release reagent can be added to allow the bound antibody to spontaneously dissociate. In various embodiments, the method further comprises expanding the cells (e.g., in vitro) to dilute the bound antibody. In various embodiments, the method further comprises expanding the cells and subsequently removing those cells that have the antibody bound thereto. Advantageously, dilution and / or removal of the antibody minimizes or eliminates the problem of the antibody interacting with the host immune system (e.g., the human immune system) when the cells (e.g., neutrophil progenitor cells and their progeny) are infused into a host, particularly when the antibody is from a different species than the host (e.g., when mouse-derived monoclonal antibodies are used to isolate neutrophil progenitor cells for infusion into a human).

[0071] In comparison to using hematopoietic stem progenitor cells (HSPCs) as starting material and expanding neutrophils from these cells, embodiments of the present method provide a more efficient way of producing neutrophils, as HSPCs can differentiate and expand into multiple cell lineages, resulting in lower yield and longer processing time. A reliable supply of neutrophils that can be produced by embodiments of the present method can be advantageously used for therapy or research (e.g., in vitro research).

[0072] Neutrophil progenitor cells can be used for large-scale production of neutrophils for, e.g., bone marrow ablation and treatment of neutropenic patients (e.g., cell therapy) and for research. Accordingly, in various embodiments, a composition enriched with neutrophil progenitor cells and / or their progeny, e.g., the neutrophil progenitor cells have at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, or at least about 8 of the following expression profiles: CD71 高 / + , LOX-1 中 / 低 / - , CD164 高 / + , CD112 高 / + , CD181 中 / 低 / - , TACSTD2 高 / + , CD11b 低 / - , and / or CD49d 中 / 高 / + is provided. In one embodiment, the composition is enriched with CD71 高 / +neutrophil progenitor cells and / or progeny thereof. In various embodiments, the amount / concentration of neutrophil progenitor cells and / or progeny thereof contained in the enriched composition is higher than the amount / concentration of neutrophil progenitor cells and / or progeny thereof naturally occurring in an animal or human biological sample, such as blood and bone marrow. In various embodiments, the composition further comprises one or more blood components, such as red blood cells, various types of white blood cells, and platelets. In various embodiments, the composition comprises a component derived from / obtained from bone marrow and / or blood (e.g., umbilical cord blood, peripheral blood, etc.) that is subsequently enriched. In various embodiments, the composition comprises a component derived from / obtained from human bone marrow and / or human blood.

[0073] In various embodiments, a composition is provided that consists of neutrophil progenitor cells and / or progeny thereof, e.g., the neutrophil progenitor cells have at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, or at least about 8 of the following expression profile: CD71 高 / + , LOX-1 中 / 低 / - , CD164 高 / + , CD112 高 / + , CD181 中 / 低 / - , TACSTD2 高 / + , CD11b 低 / - , and / or CD49d 中 / 高 / + . In various embodiments, the composition consists of neutrophil progenitor cells that express CD71 and / or CD71 高 / + neutrophil progenitor cells. In various embodiments, the composition consists of neutrophils derived from neutrophil progenitor cells, e.g., neutrophil progenitor cells that have at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, or at least about 8 of the following expression profile: CD71 高 / + , LOX-1 中 / 低 / - , CD164 高 / + , CD112 高 / + , CD181 中 / 低 / - , TACSTD2 高 / + , CD11b 低 / - , and / or CD49d 中 / 高 / + , or neutrophil progenitor cells that express CD71 or CD71 高 / + neutrophil progenitor cells.

[0074] In various embodiments, the composition comprises a therapeutic composition. In various embodiments, there is provided an embodiment of the composition for use in therapy. In various embodiments, there is provided an embodiment of the composition for use in cell therapy. In various embodiments, there is provided an embodiment of the composition for use in treating a condition selected from the group consisting of a blood disorder, a bone marrow disorder, an immune system disorder, a cancer, a blood cancer, an infection (e.g., a bacterial and / or fungal infection), neutropenia, multiple myeloma, leukemia, lymphoma, aplastic anemia, thalassemia, sickle cell disease, severe combined immunodeficiency syndrome, and the like. In one embodiment, there is provided an embodiment of the composition for use in treating neutropenia.

[0075] In various embodiments, there is provided an embodiment of the use of the composition in the manufacture of a medicament for treating a condition selected from the group consisting of a blood disorder, a bone marrow disorder, an immune system disorder, a cancer, a blood cancer, an infection (e.g., a bacterial and / or fungal infection), neutropenia, multiple myeloma, leukemia, lymphoma, aplastic anemia, thalassemia, sickle cell disease, severe combined immunodeficiency syndrome, and the like. In one embodiment, there is provided an embodiment of the use of the composition in the manufacture of a medicament for treating neutropenia.

[0076] In various embodiments, there is provided a method of treating a condition in a subject, the condition selected from the group consisting of a blood disorder, a bone marrow disorder, an immune system disorder, a cancer, a blood cancer, an infection (e.g., a bacterial and / or fungal infection), neutropenia, multiple myeloma, leukemia, lymphoma, aplastic anemia, thalassemia, sickle cell disease, severe combined immunodeficiency syndrome, and the like, the method comprising administering to the subject the composition. In some embodiments, the composition is derived from the subject (e.g., prior to enrichment). In some embodiments, the composition is not derived from the subject. In some embodiments, the composition is derived from a donor. In some embodiments, the method comprises a method of treating neutropenia comprising administering CD71 高 / + or CD71 -expressing neutrophil progenitor cells (proNeu) to a neutropenic patient.

[0077] In various embodiments, embodiments are provided of the use of the composition in the manufacture of an infusion composition, e.g., for treating a condition selected from the group consisting of a blood disorder, a bone marrow disorder, an immune system disorder, a cancer, a blood cancer, an infection (e.g., a bacterial and / or fungal infection), neutropenia, multiple myeloma, leukemia, lymphoma, aplastic anemia, thalassemia, sickle cell disease, severe combined immunodeficiency syndrome, and the like. In one embodiment, embodiments are provided of the use of the composition in the manufacture of an infusion composition for treating neutropenia.

[0078] In various embodiments, a method of making an infusion composition or a composition for transplantation, e.g., for a subject such as a neutropenic subject, is provided, the method comprising: enriching a composition (or a starting composition) for neutrophil progenitor cells and / or progeny thereof, e.g., neutrophil progenitor cells having at least one of the following expression profiles: CD71 高 / + , LOX-1 中 / 低 / - , CD164 高 / + , CD112 高 / + , CD181 中 / 低 / - , TACSTD2 高 / + , CD11b 低 / - , and / or CD49d 中 / 高 / + . In various embodiments, the method comprises enriching the composition (or the starting composition) for neutrophil progenitor cells and / or progeny thereof that express CD71 or CD71 高 / + . In various embodiments, the composition (or the starting composition) is derived from / obtained from bone marrow and / or blood (e.g., umbilical cord blood, peripheral blood, etc.). In various embodiments, the composition (or the starting composition) is derived from / obtained from human bone marrow and / or blood. The composition (or the starting composition) can be derived from / obtained from a subject or a donor. In some embodiments thereof, the method comprises obtaining the composition (or the starting composition) from a subject prior to the enriching.

[0079] In various embodiments, the enriching step comprises contacting the composition (or the starting composition) with one or more agents (e.g., molecules capable of binding to one or more biomarkers as described herein) for detecting the biomarker(s). In various embodiments, the enriching step comprises contacting the composition (or the starting composition) with one or more antibodies against one or more of CD71, LOX-1, CD164, CD112, CD181, TACSTD2, CD11b, and / or CD49d.

[0080] In various embodiments, a method for producing mature neutrophils for transplantation is provided, comprising the steps of isolating a subset of neutrophil progenitor cells (proNeu) from a human biological sample, such as human umbilical cord blood and fetal bone marrow, including using CD71 as a selection marker; and culturing the isolated neutrophil progenitor cell population, for example, for three days in a serum-free medium containing a myeloid expansion supplement for expansion.

[0081] In various embodiments, a method for identifying an agent that increases the amount / concentration of neutrophils and / or neutrophil progenitors in a composition or a subject is provided, the method comprising: determining the amount / concentration of neutrophils and / or neutrophil progenitors in a first sample obtained from the composition or the subject at a first time point before the composition or the subject is administered / treated with the candidate agent, determining the amount / concentration of neutrophils and / or neutrophil progenitors in a second sample obtained from the composition or the subject at a second time point after the composition or the subject is administered / treated with the candidate agent, and comparing the amount / concentration of neutrophils and / or neutrophil progenitors in the first sample and the second sample. In various embodiments, the method further comprises inferring that the candidate agent is an agent capable of increasing the amount / concentration of neutrophils and / or neutrophil progenitors when the amount / concentration of neutrophils and / or neutrophil progenitors in the second sample is increased relative to the first sample, and inferring that the candidate agent is not an agent capable of increasing the amount / concentration of neutrophils and / or neutrophil progenitors when the amount / concentration of neutrophils and / or neutrophil progenitors in the second sample is not increased relative to the first sample. In various embodiments, determining the amount / concentration of neutrophils and / or neutrophil progenitors in the first and / or second samples comprises determining the amount / concentration of cells having at least one of the following expression profiles: CD71 高 / + 、LOX-1 中 / 低 / - 、CD164 高 / + 、CD112 高 / + 、CD181 中 / 低 / - 、TACSTD2 高 / + 、CD11b 低 / - and / or CD49d 中 / 高 / + In various embodiments, determining the amount / concentration of neutrophils and / or neutrophil progenitor cells in the first and / or second sample comprises determining the amount / concentration of neutrophils and / or neutrophil progenitor cells expressing CD71 or CD71 高 / + Cell amount / concentration.

[0082] In various embodiments, a kit, optionally a kit for use in the methods described herein, is provided comprising an agent for detecting a biomarker, e.g., a molecule capable of binding to or having affinity for the biomarker. In various embodiments, the kit comprises at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, or at least about 8 molecules capable of binding to at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, or at least about 8 of the biomarkers as described herein. In various embodiments, the kit comprises a molecule(s) for binding to at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, or at least about 8 biomarkers selected from the group consisting of CD71, LOX-1, CD164, CD112, CD18, TACSTD2, CD11b, and CD49d. In various embodiments, the kit comprises an antibody or antibodies for binding to at least about 1, at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, or at least about 8 biomarkers selected from the group consisting of CD71, LOX-1, CD164, CD112, CD181, TACSTD2, CD11b, and CD49d.

[0083] In various embodiments, a method or product as described herein is provided. BRIEF DESCRIPTION OF DRAWINGS

[0084] Figure 1 . Single-cell RNA sequencing analysis revealed that GMPs are a heterogeneous group of progenitors. (A) Existing and annotated single-cell datasets containing mouse BM GMPs were reanalyzed. Normalized counts were downloaded for each dataset and analyzed using the Seurat package. Louvain clustering was performed and annotations were assigned based on key lineage-associated genes. (B) Mouse BM CMPs, Ly6C - GMPs and Ly6C + Gating strategy for GMPs. Colored dots represent indexed sorted cells for Smart-Seq2 scRNA-Seq. (C) (Top) Monocle2 trajectory prediction on cells sequenced in (B). Cells were arranged from left to right according to pseudotime. Branch points were indicated and (bottom) display of description for each lineage. (D) Branch point #1 analysis of lineage-associated genes and their expression across pseudotime between granulocytes (black line) and monocytes (gray line) (see also Figure 8D). (E) Branch point #2 analysis of lineage-associated genes and their expression across pseudo-time between neutrophils (black line) and eosinophils (gray line) (see also Figure 8 E). (F) PCA mapping of total transcripts for the CMP and GMP subsets and sorted downstream neutrophil (preNeu) and monocyte (TpMo) precursors. (G) Integration of Smart-Seq2 sequenced cell datasets with 10x (Tabula Muris) whole mouse BM datasets using Seurat v3. Data integration quality is reflected in the overlay plot and (H) respective plots for each dataset indicating cell identity and relative cluster positioning. (see also Figure 8 ).

[0085] Figure 2InfinityFlow resolves GMP heterogeneity at the proteome level. (A) Workflow of InfinityFlow. Whole mouse BM cells are stained with a backbone panel of lineage markers (see Methods) to allow for the differentiation of each lineage to generate a UMAP. Cells are then split into wells containing PE-conjugated antibodies. Information from each well is then recorded by flow cytometry and processed using the InfinityFlow pipeline. (B) t-SNE dimensionality reduction analysis of 81 GMP staining markers from InfinityFlow data. Phenograph clustering was performed and clusters were grouped to each myeloid lineage based on known lineage-restricted markers. (C) Analysis of the top discriminating markers for each cluster resulting from (B). Black arrows (top) indicate known lineage-specific surface markers, indicating GMP subset identity (right). Data is represented as Z-scores based on predictive Log2 mean fluorescence intensity (MFI) from high (dark) to low (light). Importantly, each section marker (demarcated by the space between heatmaps) is a high expression marker representing a certain progenitor subset of GMPs. (D) UMAP map of total mouse BM cells representing measured cKit expression levels from high (dark) to low (light) as log2 MFI. Neu = neutrophils, preNeu = pre-neutrophils, Eo = eosinophils, T,B = T and B cells, NK = natural killer cells, Mo = monocytes, Baso = basophils, DC = dendritic cells, cMoP = common monocyte progenitor, GMP = granulocyte-monocyte progenitor. (E) Zoomed-in expression level (Log2 MFI) plot of the GMP region (dashed box in (D)) representing measured intensities of backbone markers. Expression continuity from GMPs to preNeu is shown (highlighted box) for CD34 and CD1 1 b expression. Cell subset regions are indicated (bottom left), indicating a bridge of progenitors. (F) Putative markers identified by InfinityFlow for GMP subsets. Zoomed-in plots are represented as predictive intensities (Log2 MFI) from low (light) to high (dark). (See also Figure 2) Figure 9 ).

[0086] Figure 3 . Flow cytometry analysis of BM GMPs revealed CD106 - CD1 1 b 低 and CD106 + CD1 1 b 高Neutrophil progenitor populations. (A) Gating and sorting strategy of mouse BM myeloid cell populations. CMP = common myeloid progenitor, cMoP = common monocyte progenitor. (B) Representative micrographs of sorted populations shown in (A) (n = 3). Scale bar = 10 um. Grey arrow indicates vacuolization. (C) PCA analysis using bulk RNA-seq data from neutrophil and monocyte precursors sorted according to the gating strategy in (A). (D-E) Normalized expression of a variety of key monocyte (D) and neutrophil (E) transcription factors was compared. Results are expressed as mean Log2 RPKM (n = 3) ± SD. (F) Heatmap of the top 5% of mouse transcription factors with the highest variability in the indicated subset. Known key factors are highlighted in bold. ProNeu-specific genes are highlighted as indicated (black boxes). Data are expressed as Z-score of expression (Log2 RPKM) calculated per gene according to levels from high (dark) to low (light). Importantly, key differential genes for each lineage are shown in bold on the right. (See also Figure 10 ).

[0087] Figure 4 . Neutrophil development depends on two committed proNeu populations. (A) Representative FACS plots of gated proNeul and proNeu2 in WT and Cebpe - / - mice. (B) Absolute counts of BM myeloid progenitors from WT and Cebpe - / - mice. Data are expressed as mean ± SD (n = 5 per group) and are representative of two independent experiments, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001 (Student’s t test). (C) BM chimeras were prepared by reconstituting irradiated mice with an equal proportion of WT CD45.1 + and Cebpe - / - CD45.2 + cells. Percentage of contribution of WT CD45.1 + or Cebpe - / - CD45.2 + cells to various hematopoietic cells is expressed as mean ± SD (n = 5) and is representative of two independent experiments. **** = p < 0.0001 (Student’s t test). (D) UMAP analysis of total live BM cells from WT and Cepbe - / - mice. Cells were manually gated and overlaid onto the UMAP plot, representing the positioning of each subset in the UMAP space. (E) Sorted populations were stimulated in vitro with CSF-1, and F4 / 80 +Macrophages were quantified. Results are expressed as mean (n=4-5 per subset) ± SD and represent three independent experiments. (F) Sorted uGFP + proNeu1, CD45.2 + proNeu2 and ROSA mT / mG RFP + preNeu to wild-type CD45.1 + Experimental setup (top) and analysis time points (bottom) for the experiment of recipient engraftment of BM transmigrated (bottom) by proNeu1 and proNeu2 (top). Black dots represent transmigrated subsets after 3 days. Data represent 5 recipient mice from two independent experiments. Mo = monocytes, Eo = eosinophils. (See also Figure 11 ).

[0088] Figure 5 proNeu1 and proNeu2 are functionally distinct progenitors. (A) Heatmap of total ANOVA corrected variable genes from bulk RNA-Seq transcripts of sorted precursor subsets. Data are expressed as Z-scores, from low (light) to high (dark) expression. Gene clusters were defined according to hierarchical clustering method (1 to 3) and used to perform (B) Gene Ontology (GO) enrichment analysis using EnrichR, showing the main GO terms (biological process, cellular component and molecular function). (C) In vitro proliferation assay of sorted populations over 4 days. Results are expressed as mean fold change (n=3 per subset) ± SD and represent at least three independent experiments. (D) In vitro colony forming potential of sorted populations. Results are expressed as mean (n=3 per subset) ± SD and represent at least three independent experiments. *=p<0.05 (Student’s t-test). (E) Percentage of cells in the proliferative S-G2-M phase of the cell cycle, expressed by Fucci-S / G2 / M positive cells. Data represent two independent experiments. (F) Comparison of GO enrichment and biological function enrichment analysis of proNeu1 and proNeu2. Biological function enrichment analysis was performed using the Ingenuity Pathway Analysis (IPA) tool. (G) Volcano plot comparing BM proNeu1 and proNeu2. Selected DEGs corresponding to proNeu1 and proNeu2 functions are labeled on the plot. (H) Experimental setup and analysis time points for the intermediate cecal ligation and puncture (CLP) sepsis kinetics. (I) Absolute counts of total BM GMP (left) and Ly6C + GMP composition at the indicated time points. *=p<0.05 (one-way ANOVA). (K) Absolute counts of total BM GMP (left) and Ly6C +Absolute counts of GMP subsets (right). (H-K) Data are presented as mean (n=4-9 per time point) ± SD and are representative of two independent experiments. * = p<0.05 (one-way ANOVA). (See also Figure 12 ).

[0089] Figure 6 . G-CSF directs a biased specification of GMP towards neutrophil commitment during emergency granulopoiesis. (A) Septic BM Ly6C - GMP vs. WT sham control BM Ly6C - Transcriptomic comparison between GMP and WT sham control. Selected genes represent a variety of genes critical for multiple myeloid lineages. (B) Cytokine analysis of serum from mid-septic mice at different time points. Data are presented as mean (n=4-9 per time point) ± SD from two independent experiments. * = p<0.05, *** = p<0.001, **** = p<0.0001 (one-way ANOVA). (C) Analysis of Ly6C + GMP subsets. Data are presented as mean (n=5 per time point) ± SD and are representative of two independent experiments. * = p<0.05 (one-way ANOVA). (D) Bone marrow ablation experimental schedule (left) and analysis of Ly6C + GMP composition. Lines represent total Ly6C + Absolute counts of GMP. (E) Absolute numbers of bone marrow mature neutrophils and blood neutrophils at the indicated time points. (D-E) Data are presented as mean (n=5 per time point) ± SD and are representative of two independent experiments. ** = p<0.01 (one-way ANOVA). (See also Figure 13 ).

[0090] Figure 7 . InfinityFlow characterization of human cord blood revealed 2 proNeu subsets. (A) UMAP of total (lysed red blood cells) cord blood cells. Each cluster was annotated according to its marker profile. T, B, NK cells were clustered together as their marker groups were grouped together as lineage exclusion markers. (B) Expression plot representing the expression of the backbone markers of various neutrophil subsets. Results are presented as scaled fluorescence intensity. (C) InfinityFlow predicted expression levels of putative markers for proNeu identification. Results are presented as scaled predicted fluorescence intensity from low (light) to high (dark) levels. (D) Gating on proNeu (left) revealed a rare SSC 低 CD49d+ proNeu1 and SSC 高 CD49d 中 proNeu2 subset (right). Since proNeul cells are rare, the FACS plot for the proNeu subset was stitched from all LEGENDScreen™ sample files. (E) Histogram of fluorescence intensity for the proNeu subset. (F) FACS analysis of neutrophil subsets from umbilical cord blood, peripheral blood, and fetal bone marrow samples. (G) Heatmap generated and plotted for simultaneous comparison of the highest expressed markers (MFI >10 3 ) from each subset. Expression is represented as Z-score of Log2 MFI normalized by marker, from low (light) to high (dark) levels. (See also Figure 14 ).

[0091] Figure 8 .GMP is a heterogeneous progenitor population that includes known monocyte progenitors. (A) (Top) Gating strategy for MDP (monocyte-dendritic cell progenitors) shown in dark grey and cMoP (common monocyte progenitors) shown in light grey according to (Hettinger et al., 2013). (Bottom) Reverse gating of each population and overlay onto traditional gating of GMP (granulocyte-monocyte progenitors) is shown. (B) (Left) Gating strategy for BM progenitors according to (Liu et al., 2019) and (Right) overlay of gated populations with conventional GMP gating strategy. (C) Sorting strategy for preNeu and TpMo for single cell sequencing. (D-E) Branch expression analysis modeling (BEAM) using Monocle2 for (D) Branch Point #1 and (E) Branch Point #2. Gene expression is represented from low (light) to high (dark) levels. (F) Gene expression analysis of neutrophil-related genes in the indicated subsets. (G) mRNA and corresponding protein marker expression levels plot for index sorted cells.

[0092] Figure 9 .UMAP with InfinityFlow reveals expression continuity across multiple BM lineages. (A) Developmental continuity of cell lineages across multiple myeloid lineage expression. (B) Unbiased clustering of InfinityFlow dataset using PhenoGraph. Identity of clusters was identified using canonical lineage markers as shown in Table 2. (C) UMAP plot of known lineage-restricted markers, from low (light) to high (dark) expression levels, indicating each cell type.

[0093] Figure 10 .proNeu2 is a CD34 低 CD11b 高Progenitor cells. (A) Localization of proNeu1 and preNeu1 in the integrated dataset and (B) differential expression profiles of the clusters shown. This expression data is derived only from the unintegrated Tabula Muris dataset. (C) GMP gating strategy without CD11b exclusion shows CD11b 低 and CD11b 高 proNeu. (D) FACS analysis of downregulation of CD115 (CSF-1R) expression during tissue processing with increasing temperature. Data are representative of at least three independent experiments. (E) Frequency and absolute counts of neutrophil progenitor cells in mouse bone marrow. Data are expressed as mean ± SD (n = 5) and are representative of three independent experiments.

[0094] Figure 11 Neutrophil development depends on two committed proNeu populations. (A) Neutrophil development from WT (left) and Cebpe - / - (Right) UMAP of total BM of a mouse, showing multiple cell lineages. Cells were manually gated and overlaid on the UMAP to visualize the location of each population and its relationship to adjacent cell types. (B) (Top) UMAP expression of CD81 and CD106, showing the localization of proNeu2. (Bottom) UMAP expression heatmap of CD115 and SiglecF, indicating that Cebpe - / - Atypical granulocyte populations in BM cells. (C) The sorted populations were stimulated in vitro with M-CSF and Ly6G was expressed on days 3 and 5. + Neutrophils were quantified. The results are expressed as mean (n=3 per subset) ± SD and are representative of three independent experiments. (D) The sorted uGFP + Ly6C - GMP, proNeu1, or proNeu2 were transferred into wild-type recipients using the BM. Results represent the progeny of the proNeu1 subset transferred 1 day after transfer. (E) Progeny composition of each transferred subset was analyzed and presented as a bar graph. Data are presented as mean ± SD (n = 4 per group) and are from two independent experiments.

[0095] Figure 12Characterization of two functionally distinct proNeu subsets. (A) Genes encoding granules in the neutrophil precursor subset and (B) ATP metabolic processes are shown as heatmaps, expressed as normalized values (Z-scores) from low (light) to high (dark) levels. Importantly, development from proNeu1 to proNeu2 to preNeu is associated with an increase in ATP metabolic processes. (C) Representative colonies of progenitor subsets at the indicated time points. Scale bar = 50 pm. (D) mRNA and protein expression of various markers in cMoP and neutrophil precursor subsets are shown as heatmaps, expressed as normalized values (Z-scores of log2RPKM (for mRNA expression) or MFI (for protein expression)) from low (light) to high (dark) levels. (E) BM Ly6C + Representative FACS plots of GMPs. (F) Representative FACS plots showing BM Ly6C + Csf1r-GFP expression of GMPs. (G) Ly6C + Percentage of subsets within GMPs. Data are expressed as mean (n = 3 per time point) ± SD and are representative of two independent experiments. * = p < 0.05 (Student’s t-test). (H) Frequency of Csf1r-GFP cells at 3 days after onset of intermediate sepsis. Data are expressed as mean (n = 3) ± SD and are representative of two independent experiments. * = p < 0.05 (Student’s t-test).

[0096] Figure 13 Bias towards neutrophil-directed specification of GMPs by G-CSF during emergency granulopoiesis. (A) Ly6C + Pearson correlation of percentage of cMoP in GMPs with spleen size measured in grams. (B) Cytokine profile of septic mice at the indicated time points after onset of sepsis. Results are expressed as relative concentrations (normalized per analyte) from low (light) to high (dark) levels.

[0097] Figure 14 (Correlation with Figure 7 CD71 specifically isolates proNeu from cord blood. (A) CD71 expression map visualized on UMAP of total cord blood cells. Expression is expressed as relative fluorescence intensity (predictive). (B) Flow cytometry validation of CD71 as putative marker for proNeu isolation. (C) Spearman’s rank correlation plot of mouse surface marker MFI with human surface marker MFI. Each point represents a shared surface marker expressed in both mouse and human (n = 140).

[0098] Figure 15In vitro differentiation capacity of the proNeu subset. CD34 高 Sorted cells for proNeu1 and proNeu2 were cultured in serum-free medium containing myeloid expansion supplement (Stem Cell Technologies) for three days. Cells were then harvested and analyzed for mature neutrophils (CD66b + CD16 + CD10 + ) progeny. Data represent frozen fetal bone marrow samples from 4 donors. Examples

[0099] The exemplary embodiments of this disclosure will be better understood through the following discussion, and if applicable, in conjunction with the Figures. It should be understood that the exemplary embodiments are illustrative, and that various modifications can be made without departing from the scope of the present invention. The exemplary embodiments are not necessarily mutually exclusive, as some embodiments can be combined with one or more embodiments to form new exemplary embodiments.

[0100] Granulocyte-monocyte progenitor cells (GMPs) are Lin - cKit + CD34 高 CD16 / 32 高 Lineage-committed progeny, derived from common myeloid progenitors (CMPs), form characteristic granulocyte-macrophage (GM) colonies in culture. GMPs are known to have the potential to generate various myeloid progeny such as neutrophils and monocytes. Since GMPs generate both monocytes and neutrophils, their potential raises important questions in situations where the demand for these two subpopulations of cells conflict with each other.

[0101] Specifically, neutrophils are produced in much greater quantities compared to monocytes, and their lineage selection requires inhibition of monocyte fate by Gfll. Furthermore, kinetic labeling studies suggest that neutrophils have much longer transit times in the bone marrow compared to monocytes. Therefore, it is currently unclear how GMPs adjust their functional output according to different demands, and whether the difference in transit times between neutrophils and monocytes is due to a distinction between downstream progenitor properties or heterogeneity already present within GMPs.

[0102] To address these issues, recent advances in single-cell transcriptomics have sought to determine the developmental cell state of each cell, which has led to the discovery of myeloid lineage and cell fate decision heterogeneity. Specifically, it has been proposed that GMPs undergo a mixed lineage state prior to granulocyte and monocyte specification. While these results provide insight into the lineage priming program in the GMP hierarchy, there is still a lack of functional validation for how the expression or repression of lineage-associated genes would translate into cell heterogeneity. Furthermore, while lineage heterogeneity within GMPs has been proposed, it is not clear whether committed progenitors already exist within these progenitors and how their behavior might differ during inflammation.

[0103] To address this heterogeneity, attempts were made to subset GMPs into Ly6C - GMPs and Ly6C + CD115 高 Monocytes (MPs) and Ly6C + CD115 低 Granulocyte progenitors (GP). However, these markers are not sufficient by themselves to fully resolve the strict lineage commitment of each progenitor subset, suggesting that better markers are needed to assess the heterogeneity of these progenitors. In particular, better markers are needed to identify neutrophil progenitors.

[0104] Neutrophils are important immune cells that provide protection against bacterial and fungal infections. Because of their short lifespan, they are continuously produced by specialized bone marrow progenitors to meet the daily demand of 100 million cells per day. Neutrophil development begins with long-lived hematopoietic stem cells that give rise to highly proliferative progenitors whose numbers increase to generate sufficient mature effector neutrophils to supply for immune surveillance and defense against microbial threats. Despite their importance, the identity and properties of committed neutrophil progenitors have not been discovered.

[0105] The inventors have previously reported the identification of neutrophil precursors called preNeu. While these precursors develop into neutrophils, they are unable to form colonies in vitro, suggesting that they are not true progenitors of neutrophils.

[0106] In the present disclosure, the inventors successfully identified in GMPs early committed progenitors responsible for the strict production of neutrophils, which they called proNeu1, through the combination of single-cell transcriptomic and proteomic analyses. A comprehensive dissection of the GMP hierarchy led to the further identification of a previously unknown intermediate proNeu2 population. Similar populations can also be detected in human samples.

[0107] Early committed neutrophil progenitors, termed proNeul, have been present in the heterogeneous population of GMPs. It was shown that proNeul give rise to intermediate progeny proNeu2, which subsequently differentiate into downstream populations. Importantly, it was found that proNeul, but not proNeu2, massively and specifically expand at the expense of monocyte differentiation in the early phase of septic inflammation.

[0108] Two subsets of neutrophil progenitors, proNeul and proNeu2, are responsible for the massive production of neutrophils in humans, characterized by different protein surface marker expression profiles, such as CD71 expression. Based on these surface markers, the inventors were able to isolate these progenitors and demonstrate their development into mature CD16 + CD10 + Neutrophils.

[0109] Overall, these findings refine the roadmap of neutrophil maturation and call for revision of the classical GMP nomenclature. The present disclosure further exemplifies the importance of understanding progenitor identity to study their function in health and disease.

[0110] Results

[0111] GMPs comprise a heterogeneous spectrum of lineage-committed progenitors

[0112] GMPs are known to give rise to granulocytes such as neutrophils and monocytes. Therefore, it is generally accepted that GMPs have oligopotent differentiation potential. However, in contrast to this assumption, recent studies have shown that GMPs are in fact a heterogeneous mixture containing lineage-committed precursors. To resolve these two views and provide a comprehensive understanding of the true hematopoietic potential of cells in this cell population, the inventors investigated the GMP population by using a repository of published single-cell transcriptome datasets (Giladi et al., 2018; Olsson et al., 2016). By extracting annotated GMPs (based on Lin - cKit + Sca-1 - CD34 高 CD16 / 32 高 ), the inventors performed a t-distributed Stochastic Neighbor Embedding (t-SNE) (Maaten and Hinton, 2008) analysis and confirmed that GMPs comprise a mixed population of monocyte, neutrophil, eosinophil, and basophil progenitor subsets ( Figure 1A). Notably, in addition to the currently published literature, the inventors discovered multiple clusters associated with different granulocyte and monocyte lineages based on known lineage-restricted genes. These multiple clusters harbor key monocyte genes such as Irf8, Ly86, and Csf1r Figure 1 A), and their gene expression correlates with the presence of common monocyte progenitors (cMoP), monocyte progenitors (MP), and mononuclear-dendritic progenitors (MDP) Figure 8 A and 8B). The inventors also observed a neutrophil-like cluster containing high expression of neutrophil elastase (Elane) and ficollin (Fcnb), which are known neutrophil lineage genes. These analyses further provide insight into the heterogeneity of GMPs and indicators of their cell lineage commitment at the transcriptomic level.

[0113] To further investigate the different GMP developmental and transition states that enable lineage commitment, single-cell RNA-Seq analysis was performed on index-sorted CMPs and total GMPs. The inventors also sorted late-stage precursors, preNeu and TpMo (transitional pre-monocytes), as reference points for neutrophil and monocyte differentiation, respectively Figure 8 C). Reverse gating of the sorted GMP populations revealed the presence of Ly6C + and Ly6C - subsets Figure 1 B). Cells were then ordered within a pseudo-time from CMPs to fully committed progenitors using Monocle (Qiu et al., 2017), allowing insight into the different developmental and trajectory states. Monocle subsequently revealed two distinct branching points Figure 1 C). Deeper analysis of these branching points revealed key fate-determining genes that change during development, suggesting a monocyte-granulocyte decision point at point 1 Figure 1 D), and a neutrophil-eosinophil decision point at point 2 Figure 1 E). These analyses revealed the initial downregulation of monocyte fate-determining genes (Ly86, Csf1r, Irf8), followed by combinatorial and selective upregulation of neutrophil-associated genes (Gfll, Rgcc, Vcaml), both of which are important for initiating a neutrophil fate Figure 8 D and 8E).

[0114] Upon further analysis using Monocle, the inventors noted the presence of a specialized progenitor population within the Ly6C + portion of GMPs at the terminal state of the neutrophil branching point Figure 1C). These cells had the highest expression of known neutrophil-specific genes, such as Gfll and S100a8, among others Figure 8 F). To determine whether this cell population could be putative neutrophil progenitors, these cells were mapped onto a principal component analysis (PCA) of total transcripts, and it was found that they clustered closer to preNeu along PC2, while departing from TpMo Figure 1 F). These putative neutrophil progenitors had low levels of CD115 and high levels of Ly6C, both at the mRNA and protein levels Figure 8 G). In addition, by performing single-cell data integration (Stuart et al., 2018) with the Tabula Muris (TM) BM dataset (Schaum et al., 2018), the inventors were able to confirm cell identities from the dataset Figure 1 G). Visualization of the integration revealed that preNeu and TpMo mapped precisely to their respective lineages, while CMPs mapped to the erythroid lineage and dendritic cell lineage Figure 1 H). Importantly, it was observed that putative neutrophil progenitors were associated with the branching point of initial neutrophils, suggesting that this population could be an early progenitor of neutrophil development Figure 1 H). Taken together, the data suggest that there is a population of putative neutrophil progenitors within the GMP, and that they are responsible for the generation of neutrophils.

[0115] InfinityFlow resolves GMP heterogeneity and identifies surface markers that discriminate the population

[0116] While transcriptomic signatures provide a useful means to determine cell states, they do not confirm cell identity or allow further downstream analysis. Therefore, to validate the heterogeneity of the GMP at the proteomic level, the expression of 261 surface markers (LEGENDScreen™, Biolegend) was assessed on total mouse BM cells by flow cytometry (Table 1). The InfinityFlow (Dutertre et al., 2019) pipeline was then used to predict the co-expression of each of the tested surface markers, and this predicted expression information was stitched into a single analysis file Figure 2 A). Out of these 81 markers, the GMP stained positively. The analysis was first restricted to these 81 markers, and then used to perform a t-SNE analysis of the GMP. With the help of PhenoGraph (Levine et al., 2015), the inventors could subset the GMP into 10 subpopulations, thereby confirming at the proteomic level that the GMP is a mixture of progenitors Figure 2B). Using the top discriminative markers of each cluster, the inventors were able to determine the identity of known monocyte (clusters 2, 6 and 7), eosinophil (cluster 5) and basophil (cluster 4) committed progenitors by their expression of lineage-specific surface markers Figure 2 C) (Arinobu et al., 2005; Hettinger et al., 2013; Iwasaki et al., 2005). However, neutrophil progenitors lack lineage-specific markers, preventing proper characterization of this dedicated progenitor.

[0117] Therefore, to circumvent this challenge, UMAP analysis was performed on the InfinityFlow dataset to understand the developmental relationships between cell types in the BM based on their protein expression (Becht et al., 2018; McInnes et al., 2018). One major advantage of UMAP is that it preserves the continuity of cell subsets, which allows the identification of rare and / or transitional populations that would be masked in t-SNE analysis (Becht et al., 2018). Extending UMAP analysis to total BM cells identified multiple cell lineages within the BM Figure 2 D), and allowed to observe a clear developmental continuum of cKit + progenitors to mature cKit - cells of basophil, eosinophil, monocyte and neutrophil lineages Figure 2 D and 9A). Using markers from InfinityFlow, each cell type was identified and annotated by co-localization of each marker in the UMAP space Figure 9 B, Table 1 and Figure 9 C). From the UMAP analysis, the inventors observed a gradual upregulation of Ly6C in GMPs (cKit + Sca-1 - CD34 + CD16 / 32 高 ) followed by a bifurcation into CD115 - and CD115 + cells, representing preNeu and cMoP, respectively Figure 2 D and 2E). While it is clear that cMoP is a subset of GMPs, the branch connecting GMPs to preNeu revealed a progressive upregulation of CD11b and downregulation of CD34 expression along the developmental continuum from GMPs Figure 2 E). This suggests that early neutrophil progenitors exist within GMPs and extend along the developmental trajectory towards mature neutrophils. Further examination revealed several differentially expressed cell surface markers, including CD81, CD49a, CD106 and CD63, which highlight this branch point (Figure 2 F). These markers can serve as positive or exclusion markers to isolate and characterize putative early neutrophil progenitors.

[0118] Identification of early neutrophil progenitors within GMP

[0119] Using the newly identified markers CD81 and CD106, the inventors characterized two phenotypically distinct neutrophil progenitors, called proNeu. This includes CD34 高 CD106 - CD11b 低 proNeu1 subset and CD34 低 CD106 + CD11b 高 proNeu2 subset Figure 3 A). This observation is consistent with the integration of scRNA-seq data, which also revealed a gap between mapped neutrophil progenitors and preNeu cells Figure 10 A), and cells within this gap expressed a continuum of neutrophil genes Figure 10 B). This suggests that there is an intermediate subset in the developmental trajectory of neutrophil development that is missing in the currently defined GMP population. Indeed, proNeu2 escapes the GMP definition as CD11b + Cells are excluded from GMP Figure 10 C). The inventors also leveraged CD81 to exclude cMoP as this marker allows to circumvent technical issues of CD115 downregulation during cell preparation Figure 10 D). Like cMoP, these neutrophil progenitors are rare, they represent 0.05-0.1% of total BM cells Figure 10 E).

[0120] Morphological analysis revealed an initial hollowing of the nucleus at the proNeu1 stage, an increase in the diameter of the hollowing at the proNeu2 stage Figure 3 B) and subsequent mature stages. Using the same sorting strategy as for neutrophil progenitors in Figure 3 A, the inventors isolated these cells and performed bulk RNA-seq on myeloid progenitors, including Ly6C - GMP, proNeu1, proNeu2, preNeu, cMoP and TpMo were subjected to bulk RNA-seq to examine their differentiation and inter-population relationships. PCA analysis revealed a clear separation of neutrophil and monocyte lineages Figure 3 C). In-depth analysis of key neutrophil and monocyte genes revealed Ly6C- Expression levels of monocyte genes in GMP and proNeul were similar Figure 3 D). These genes were then upregulated in cMoP, indicating their commitment towards a monocyte fate. Interestingly, the expression levels of the key neutrophil genes Gfil and Per3 in proNeul were also similar - in GMP Figure 3 E). By selecting the top 20% of transcription factors that were most variable between these progenitor subsets, the inventors further expanded the knowledge of myeloid transcriptional regulation and plotted them in a heatmap. This analysis revealed a set of transcription factors that were only expressed in cMoP or Ly6C - GMP. Specifically, the data also indicated that Jagl and Soxl3 are specialized neutrophil lineage commitment factors Figure 3 F). In particular, Jagl, which was shown to be associated with G-CSF-mediated neutrophil differentiation.

[0121] It has been shown that deficiency in C / EBPepsilon leads to disruption of neutrophil development. Here, RNA-Seq analysis showed that Cebpe was upregulated from proNeul to proNeu2, indicating that C / EBPepsilon can be critical for the development of early neutrophil progenitors Figure 3 E). To test this, the BM of wild-type (WT) and Cebpe - / - mice were analyzed. The analysis showed that C / EBPepsilon is critical for the development of proNeu2 Figure 4 A). Along with the loss of proNeu2, accumulation of cMoP was also observed, indicating biased differentiation towards monocyte fate due to blocked neutrophil development Figure 4 B). By generating BM chimeras with CD45.1 + WT and CD45.2 + Cebepe - / - BM cells at a 50:50 ratio, the observations were further confirmed. Analysis of the BM of these mice showed that proNeu2 and downstream populations were mostly generated from WT CD45.1 + cells Figure 4 C). This indicates the importance of proNeu2 as a bridge between proNeul and preNeu. UMAP further illustrates this, indicating that proNeu2 acts as a bridge between proNeul and preNeu development and that the absence of these cells leads to aberrant differentiation pathway, generating atypical granulocytes, i.e. SiglecF + CD115 + ( Figure 4 D and 11B).

[0122] Since proNeu1 has both monocyte and neutrophil transcription factors ( Figure 3 D and 3E), we hypothesized that proNeu1 might not be fully specialized to the neutrophil lineage and might generate monocytes according to lineage cues. To assess the fate of proNeu, various sorted progenitor cell populations were cultured with CSF-1, which is known to bias differentiation toward the monocytic lineage. The results showed that both proNeu1 and proNeu2 produced Ly6G in response to CSF-1. + Neutrophils, not macrophages ( Figure 11 C), while cMoP and Ly6C - GMP produced mostly macrophages by the fifth day of culture with CSF-1 ( Figure 4 E) To further confirm the neutrophil targeting ability of proNeu1 and proNeu2, the present inventors adoptively transferred sorted Ly6C - GMP, proNeu1, proNeu2 and track their differentiation potential. Ly6C - GMP generates proNeu1 and cMoP after 1 day, while proNeu1 generates proNeu2 and subsequent neutrophil subsets ( Figure 11 D). Similarly, proNeu2 specifically produces only preNeu and immature Neu ( Figure 11 E).

[0123] Since these transfer experiments were performed in separate WT recipients, the inventors wanted to evaluate the differentiation potential of proNeu in the same microenvironment. To this end, the inventors co-transferred sorted and labeled proNeu (1 & 2) and preNeu into WT recipients and tracked their development after 3 days. The results showed that both proNeu1 and proNeu2 could only generate neutrophils, and their monocyte differentiation potential was negligible ( Figure 4 F). Furthermore, a stepwise differentiation progression from proNeu1 to preNeu was observed, with sequential upregulation of Ly6G and CXCR2. Together, these data highlight the existence of programmed neutrophil lineage commitment in GMPs, which begins with proNeu1 and specializes in downstream neutrophil subsets through C / EBPε-dependent proNeu2 development.

[0124] ProNeu1 and proNeu2 are functionally distinct populations of early neutrophil progenitors.

[0125] To dissect the functional heterogeneity and progression of these neutrophil progenitors, we analyzed the- GMP to preNeu transcriptomic signatures. By highlighting the top variable genes in each subset, the inventors came up with three different gene clusters that are differentially regulated between each stage of neutrophil development Figure 5 A). From the heatmap, the inventors observed a sequential increase in granule production (cluster 1) Figure 5 B and 12A) and concomitant loss of mitochondrial and ribosomal genes (cluster 3), which is consistent with their progressive acquisition of neutrophil functionality Figure 5 B and Figure 12 B).

[0126] As CD34 is a known marker of hematopoietic progenitors, it was wondered whether progenitor properties were lost in the proNeu2 subset. To test this, the proliferative potential of proNeu1 and proNeu2 was assessed. In vitro culture showed that both proNeu1 and proNeu2 had a high proliferative potential, unlike the minimal colony-forming activity of preNeu 低 proNeu2 subset. To test this, the proliferative potential of proNeu1 and proNeu2 was assessed. In vitro culture showed that both proNeu1 and proNeu2 had a high proliferative potential, unlike the minimal colony-forming activity of preNeu Figure 5 C). Notably, the proliferative capacity of proNeu2 was observed to be lower than proNeu1, which was further supported by the fewer colonies generated in vitro by proNeu2 Figure 5 D and 12C). Unlike proNeu2, proNeu1 had an enhanced colony-forming potential, with a cell cycle activity similar to that of uncommitted Ly6C - GMP, as shown by the Fucci cell cycle transgenic reporter line (Sakaue-Sawano et al., 2008) Figure 5 E). This suggests that proNeu1 has higher self-renewal properties, which are then exchanged for effector functions in proNeu2. Moreover, transcriptomic pathway analysis supported this decrease in proNeu2 progenitor functions, showing a specific enrichment in neutrophil effector functions, while proNeu1 was enriched in cellular components and cell survival Figure 5 F). Altogether, the data show that proNeu1 and proNeu2 are two distinct progenitor subsets with unique functions related to neutrophil development. Similar to their surface marker expression, Cd34 was found to be upregulated in proNeu1, while genes encoding CD106 (Vcam1) and CD11b (Itgam) were significantly enriched in proNeu2 Figure 5 G). Thus, these results confirmed the scRNA-seq and UMAP Figure 10 A) analysis that proNeu2 is downstream of proNeu1 in the neutrophil lineage.

[0127] To understand how these progenitors play a differential role in inflammatory states, the inventors took advantage of a sepsis model and tracked the composition of BM progenitors at different time points Figure 5 H). Throughout the infection, the inventors observed a Ly6C + GMP expansion, but a Ly6C - GMP number changed very little Figure 5 I). Further analysis of Ly6C + GMP revealed a biased differentiation effect towards the neutrophil lineage at day 3, with a specific increase in proNeul frequency at the expense of cMoP number Figure 5 J). Unlike proNeul, proNeu2 remained essentially unchanged throughout the infection, indicating a differential role of each progenitor during sepsis. The inventors also showed that this biased effect could be observed as early as day 1 of sepsis onset Figure 12 E and 12G), and confirmed that CD115 was not downregulated during inflammation using Csf1r-GFP MaFIA transgenic mice Figure 12 F and 12H). Then, by day 9, the shift in myeloid progenitor potential was restored to physiological frequencies, along with a specific expansion of cMoP Figure 5 K), indicating their importance during the resolution phase of sepsis. Importantly, this critical information would have been lost if total GMP were analyzed, demonstrating the need to analyze specific progenitor subsets to understand the dynamics of progenitor function in inflammatory states.

[0128] G-CSF-mediated BM bias of myeloid progenitors modulates neutrophil expansion

[0129] In infections and diseases requiring a high demand of mature cells, the ability of BM progenitors to replenish the immune cell circulation pool is a key step. In particular, neutralizing invading pathogens and microbial insults requires an emergency granulopoiesis, a mechanism by which the immune system adapts to produce and mobilize neutrophils in a rapid and efficient manner.

[0130] To determine whether the increase in proNeul frequency during sepsis was preset in early progenitors, a transcriptome analysis was performed on Ly6C - GMP at day 3 of sepsis onset, revealing a downregulation of multiple lineage-associated genes such as Mcpt8, Ly86, Prss34 and Fcera1. Upregulation of granulin genes such as S100a9, S100a8, Chil3 and Lcn2 was also observed Figure 6A). These results not only validate the importance of the proNeu discovery, but further confirm the biased effect of proNeul differentiation. Interestingly, at day 6 when the variability of the BM bias reaches its peak, the inventors observed a correlation between the size of the spleen and the recovery of the biased progenitor compartment Figure 13 A), suggesting that extramedullary hematopoiesis can play a role in the resolution of sepsis.

[0131] To determine the factors and signals able to enable proNeul and proNeu2 expansion, the inventors performed an inflammatory analyte screen on mouse sera at different time points Figure 13 B). Specifically, high levels of G-CSF and IL-6 were found at day 3, which can explain the neutrophil potential bias Figure 6 B). This was supported by the significant increase in proNeu frequency by in vivo administration of G-CSF Figure 6 C). To demonstrate this enlightening biased effect, the chemotherapy drug 5-fluorouracil was used to analyze BM cells from mice subjected to bone marrow ablation and G-CSF was introduced during the recovery of the myeloid compartment. The introduction of G-CSF at day 9 showed a significant increase in proNeul frequency at day 12 after bone marrow ablation Figure 6 D), and this translated into a much higher production of neutrophils at day 15 Figure 6 E). Altogether, these data highlight the relevance of the proNeu discovery by providing a framework to understand sepsis biology via the appreciation of progenitor bias. Notably, the results support a G-CSF-mediated biased effect on proNeu commitment at an early stage of the inflammatory phase of sepsis, which is subsequently compensated by high monocyte production and commitment to effectively resolve sepsis. InfinityFlow on human cord blood cells revealed proNeul and proNeu2 subsets

[0132] To determine whether such progenitors exist in humans, the same workflow (i.e., the InfinityFlow pipeline) was applied to examine whole human cord blood cells to screen for differential surface markers that can help identify these progenitors.

[0133] Briefly, red blood cell (RBC)-lysed cord blood cells were stained with a common backbone set of surface markers representing the various cell lineages present in cord blood. Thereafter, cells were split into wells, each well containing a unique PE-conjugated surface marker (LEGENDScreen TM). Samples were collected and the expression profile of each PE marker was processed using InfinityFlow, a machine learning application that predicts the co-expression profile of various markers with each test marker. This allowed the discovery of new subsets with high-dimensional phenotypic characterization.

[0134] UMAP analysis of total cord blood cells revealed distinct populations representing multiple lineages Figure 7 A). A deeper analysis of the neutrophil cluster revealed three previously identified subsets of myeloid neutrophils, whose protein expression profile is similar to that described in mice. Importantly, a putative proNeu population was identified by UMAP, which has low expression levels of CD11b and high levels of CD49d Figure 7 B). Through mining of the InfinityFlow dataset, the inventors further identified several potential markers for human proNeu identification. These include CD71, LOX-1, CD164, CD112, CD181 and TACSTD2 Figure 7 C). Validation of one of these markers, CD71, revealed that it is expressed by proNeu only in RBC-lysed total cord blood cells Figure 14 A). This suggests a possible isolation strategy for proNeu Figure 14 B). This strategy is a simple two-step process that takes advantage of large volumes of donor cord blood that are typically available for clinical use. Usually, these cells are discarded after enrichment for CD34 + stem cells for cell therapeutic purposes.

[0135] The inventors can further subset proNeu into proNeu1 and proNeu2 based on their CD49d expression levels and side scatter properties Figure 7 D). CD49d 中 SSC 高 proNeu2 compared to CD49d 高 SSC 低 proNeu1 are rare cells and they express higher levels of CD34, CD38 and CD81 Figure 7 E). Expression of CD34 and CD38 suggests that proNeu1 cells exist in Lin - CD34 + CD38 + CD45RA +In the human GMP nomenclature of cells. To validate these findings, umbilical cord blood and adult peripheral blood as well as fetal bone marrow samples were analyzed. Through the analysis, the inventors were able to detect proNeul and proNeu2 in umbilical cord blood donor samples, but not in peripheral blood. In fetal bone marrow, the inventors could detect a higher frequency of proNeul, as well as a higher frequency of proNeu2, preNeu and immature Neu Figure 7 F).

[0136] The data show similarities in neutrophil development between mice and humans and identify human equivalents of proNeul and proNeu2. To further show this correlation, the inventors utilized and compared the protein expression profiles of each neutrophil subset from both the mouse and human InfinityFlow datasets. MFI was extracted from each subset and it was observed that each subset had a moderate correlation between mouse and human equivalent neutrophil subsets Figure 14 C). Furthermore, by comparing the top expressed markers of each subset in both mouse and human, 16 markers were identified that share a similar expression progression from proNeul to mature neutrophil Figure 7 G). This further indicates a similar functional gain and maturation process in mice and humans. In summary, this data provides a complete analysis of the human neutrophil pathway, identifying human equivalents of proNeul and proNeu2 and their associated surface marker expression profiles.

[0137] Finally, to demonstrate that the identified progenitor cells proNeul and proNeu2 have the potential to generate mature neutrophils, sorted proNeul and proNeu2 cells were cultured in vitro. The data show that both proNeul and proNeu2 are able to generate CD16 + CD10 + mature neutrophils Figure 15 This generation takes 3 days, which if transferred into patients receiving myeloablative therapy, can provide a rapid supply of neutrophils. This is in contrast to CD34 高 This finding also allows for the further development of culture conditions that would allow for the prolonged proliferation and expansion of donor neutrophils for therapeutic purposes. In summary, these data strongly support the existence of two different neutrophil progenitor cells and a method for isolating these cells for therapeutic use.

[0138] Discussion

[0139] The classical model of hematopoiesis is a hierarchical and stepwise differentiation program guided by instructive transcription factors that control the lineage fate of each cell. The production of myeloid cells is performed by GMP (Lin - cKit + Sca-1 - CD34 + CD16 / 32 高 ) as shown by the formation of both granulocytic and monocytic colonies in a methylcellulose colony-forming assay (Akashi et al., 2000). However, whether GMP are truly homogeneous multipotent subsets or a heterogeneous mixture of lineage-restricted populations has remained a long-standing question. To better delve into this, the inventors adopted a combination of transcriptomic, proteomic and bioinformatic tools to develop a phenotypic profiling approach for the identification of putative progenitor cells within the GMP population. By profiling the currently defined GMP using an optimized defined subset of surface markers derived from InfinityFlow (Dutertre et al., 2019) and bioinformatic approaches, the inventors identified a population of neutrophil-committed progenitor cells (i.e., CD81 + CD106 - proNeu1). Moreover, by deep surface marker phenotypic profiling and RNA-Seq analysis, the inventors further characterized a subset of downstream CD81 + CD106 + proNeu2 progenitor cells. Altogether, these data serve as missing links in the early stages of the neutrophil developmental pathway, which allowed to map the developmental process from proNeu1→ proNeu2→ preNeu→ immature neutrophil→ mature neutrophil.

[0140] The ability to delineate early neutrophil progenitor cells from GMP allowed to assess transcription factors involved in instructing myeloid progenitor lineage specification and commitment. Examination of a list of TFs known to control monocyte and granulocyte lineage commitment (Irf8, Klf4, Irf5, Gfll, Cebpe and Per3) consistently showed that Ly6C -GMP and proNeul express similar levels of these TFs, while differential expression of these TFs only becomes apparent at the proNeu2 and cMoP stages. This suggests that the neutrophil-monocyte lineage bifurcation can occur at this hierarchical level. This raises the question of the extent of lineage commitment of the proNeul population, i.e., whether this subset of progenitor cells is specialized or committed towards the neutrophil lineage. Functional studies in vitro and in vivo show that proNeul are committed towards the neutrophil lineage, as proNeul only give rise to neutrophils even when they are cultured with strong monocyte lineage-promoting cytokines such as M-CSF. In addition, RNA-Seq data show that cMoP, Ly6C - GMP, proNeul and proNeu2 have distinct "TF signatures" that provide an overview of putative TFs that can be important for lineage specification and commitment of these progenitor cells. For example, the inventors observed an increase in Jagl and Sox 13 expression levels, specifically in proNeul, suggesting that these two TFs can be developed as markers for proNeul and examined as key regulators of proNeul differentiation. Overall, these data not only confirm previous lineage-associated TFs, but also expand the list of putative TFs for myeloid lineage specification and commitment.

[0141] A previous in vivo tracking study demonstrated that granulocytes and monocytes are closely related in their clonal origin. Here, the disclosure describes transcriptionally and molecularly defined neutrophil progenitor cells in the GMP hierarchy, allowing a better understanding of lineage relationships and dynamics of neutrophil / monocyte production. Given that myeloid progenitor cells can be maintained in the absence of HSC input, it is conceivable that these progenitor cells are highly adaptable to different inflammatory perturbations depending on the demand of the immune response. This study shows that there is a selective expansion of proNeul and a selective disappearance of cMoP during the acute phase of sepsis pathogenesis. This phenomenon can be a bias in the differentiation potential of progenitor cells, which can be imagined to favor Ly6C - GMP levels, and the preferential proliferation of early neutrophil progenitor cells as a result of the direct demand for neutrophils.

[0142] In summary, the disclosure provides new insights into the divergent pathways of granulocyte progenitor cells development from GMPs towards neutrophils and monocytes, and how the balance between neutrophil / monocyte production is important for host homeostasis. Moreover, the identification of early neutrophil progenitor cells opens new avenues for therapeutic strategies to treat neutrophil deficiency in hematopoietic stem cell transplantation or high-dose chemotherapy by infusing expanded proNeu subsets. This can serve as a source of rapid neutrophil repletion to help provide protection against infection during this critical period of demand.

[0143] Materials and Methods

[0144] Mice

[0145] C57BL / 6 mice aged 8-12 weeks were housed and maintained under specific pathogen free (SPF) conditions at the BioResource Centre (BRC) of A*STAR, Singapore. Both male and female were used for experiments, but the gender and age of animals were matched as much as possible in each experiment. uGFP (C57BL / 6-Tg(UBC-GFP)30Scha / J), CD45.1 (B6.SJL-Ptprca Pepc a Pepc b / BoyJ), MaFIA (C57BL / 6-Tg(Csf1r-EGFP-NGFR / FKBP1A / TNFRSF6)2Bck / J) and Rosa26 mT / mG (STOCK Gt(ROSA) 26Sortm4(ACTB-tdTomato,-EGFP)Luo / J mice were obtained from The Jackson Laboratory. Fucci-S / G2 / M (#474) was obtained from RIKEN BioResource Center (Ibaraki, Japan; (Tomura et al., 2013)). Cebpe - / - were provided by P. Koeffler (Cancer Science Institute of Singapore, NUS, Singapore) (Yamanaka et al., 1997). All transgenic mice were maintained in C57BL / 6 background and experiments were performed after approval by the Institutional Animal Care and Use Committee (IACUC) following the guidelines of the Agri-Food and Veterinary Authority of Singapore (AVA) and the National Advisory Committee for Laboratory Animal Research (NACLAR).

[0146] Human blood, cord blood and fetal bone marrow

[0147] The acquisition of all samples followed the favorable ethical opinions of SingHealth CIRB or Institute of Medical Biology, A*STAR, Singapore. Fresh (within <24 hours from collection) human cord blood (UCB) units were collected in collaboration with the National Cancer Centre Singapore (NCCS). NCCS obtained UCB units from research license units that did not meet public clinical storage criteria through the Singapore Cord Blood Bank (SCBB). The use of samples was approved by the Central Institutional Review Board (CIRB) of the Singapore Health Services (covering NCCS) and the SCBB-specific Research Advisory Ethics Committee.

[0148] Treatment

[0149] For 5-fluorouracil (5-FU) myeloablative treatment, mice received a single intraperitoneal injection of 150 mg / kg of 5-FU (Sigma-Aldrich) or PBS control. For G-CSF treatment, mice received a single intraperitoneal injection of 1.5 pg of G-CSF / anti-G-CSF antibody complex (G-CSFcx) as previously described (Rubinstein et al., 2013). Briefly, G-CSFcx was generated by incubating G-CSF (Neupogen) and anti-G-CSF (BVD11-37G10; Southern-Biotech) at a 1 :5 cytokine to antibody ratio for 20 min at 37 °C, followed by at least 10-fold dilution in PBS prior to injection.

[0150] Tissue preparation and data analysis for flow cytometry and cell sorting

[0151] Blood was obtained by incision of the submandibular region and then lysed in red blood cell lysis buffer (eBioscience). For BM cells, mouse femurs were flushed in PBS containing 2 mM EDTA and 2% fetal bovine serum (FBS) using a 23-gauge needle and passed through a 70 pm nylon mesh screen. Spleens were harvested and homogenized into a single cell suspension using a 70 pm nylon mesh screen and a syringe plunger. Antibodies were purchased from BD, Biolegend, eBioscience, or R&D. For identification of BM myeloid progenitor subsets, cells were stained with fluorophore-conjugated anti-mouse antibodies against CD34 (RAM34), CD11b (M1 / 70), CD16 / 32 (2.4G2), CD115 (AFS598), cKit (2B8), CXCR2 (SA044G4), CXCR4 (2B11), Gr1 (RB6-8C5), Ly6C (HK1.4), CD106 (429), CD81 (Eat-2), and Flt3 (A2F10), as well as lineage exclusion markers including Ly6G (1A8), CD90.2 (53-2.1), B220 (RA3-6B2), NK.1.1 (PK136), and Sca-1 (D7). After exclusion of cell doublets and dead cells with DAPI, proNeul was identified as (Lin / CD115 / Flt3) - cKit 高 CD16 / 32 高 Ly6C + CD34 高 CD11b 低 CD106 - proNeu2 was identified as (Lin / CD115 / Flt3) -cKit 高 CD16 / 32 高 Ly6C + CD34 高 CD11b 高 CD106 + preNeu identified as (Lin / CD115 / Siglec-F) - Gr1 + CD11b + CXCR4 高 ckit 中 CXCR2 - CMP identified as Lin - cKit + Sca-1 - CD16 / 32 中 CD34 中 Ly6C identified as - GMP identified as Lin - cKit + Sca-1 - CD16 / 32 高 CD34 高 MDP identified as Lin - cKit + Sca-1 - CD115 + Flt3 + Ly6C - cMoP identified as Lin - cKit + Sca-1 - CD115 + CD81 - Flt3 - Ly6C + Flow cytometry acquisition was performed on a 5-laser BD LSRII (BD) using FACSDiva software and data were analyzed using FlowJo software (Tree Star). Cell numbers were quantified using CountBright beads (Life Technologies) according to the manufacturer’s instructions. BM neutrophil subsets were sorted using a BD ARIA II (BD) to achieve >98% purity.

[0152] LEGENDScreen TM and InfinityFlow pipelines

[0153] Mouse femur, tibia, pelvic, humerus and spinal bone marrow were collected, crushed in PBS containing 2 mM EDTA and 2% fetal bovine serum (FBS) and passed through a 70 pm nylon mesh screen. For human cord blood, one unit of cord blood donor sample was subjected to RBC lysis for 10 minutes at room temperature. Cells were centrifuged at 400g for 10 minutes. This process was repeated once to remove most of the RBC cells. Mouse cells were first stained with a fixable live / dead cell stain for 30 minutes, then stained with a backbone panel of mouse antibodies to determine various lineages in the BM. These markers include: CD34 (RAM34), CD11b (M1 / 70), CD16 / 32 (2.4G2), CD115 (AFS598), cKit (2B8), CXCR2 (SA044G4), Ly6C (HK1.4), Flt3 (A2F10), Sca-1 (D7), CD48 (HM48-1), CD43 (S11), CD62L (MEL-14) and FITC-conjugated lineage markers (Ly6G (1A8), CD90.2 (53-2.1), B220 (RA3-6B2), NK.1.1 (PK136)). After staining for 90 minutes at 4°C, cells were lysed in IX RBC lysis buffer (eBioscience) for 5 minutes and then centrifuged at 400g for 5 minutes. Cells were then stained with secondary streptavidin for 30 minutes and washed as previously described. To enrich the BM for progenitor cell identification, FITC selection kit (Stem Cell Technologies) was used to partially remove mature neutrophils, B cells, T cells and NK cells according to the manufacturer’s protocol. For cord blood cells, a backbone panel of antibodies was used to stain cells for 30 minutes at 4°C, including: CD3 (UCHT1), CD56 (HCD56), CD19 (HIB19), CD10 (HI10a), CD49d (9F10), CD34 (581), CD66b (G10F5), cKit (104D2), CD38 (HIT2), CD15 (HI98), CD14 (M5E2), CD101 (BB27), CD45 (HI30), CD11b (ICRF44), CD16 (3G8). Cells were then counted and aliquoted into individual wells containing specific PE-conjugated markers. (Tables 1 and 3). After staining for 30 minutes, plates were washed and fixed and FACS acquisition was performed on a 5-laser BD LSR II (BD) using FACSDiva software, followed by data processing through the InfinityFlow pipeline as described elsewhere (Dutertre et al., 2019).

[0154] Cytospin and Wright-Giemsa staining

[0155] Neutrophil subsets (5 × 10 each) were sorted using a Cytospin 4 cell centrifuge (Thermo scientific). 4 Cells were centrifuged onto slides, dried for 10 minutes, fixed in methanol, and stained using the Hema 3 manual staining system (Fisher Diagnostics) according to the manufacturer's protocol. Images were acquired using an Olympus BX43 equipped with a 100× oil immersion objective, and image brightness was adjusted using Photoshop (Adobe).

[0156] Transcriptomics

[0157] For single-cell transcriptome analysis, Figure 1 BM CMP, GMP, preNeu, and TpMo cells were sorted using the gating strategy described in B and 8C. Single-cell cDNA libraries were isolated using the SMARTSeq v2 protocol (Picelli et al., 2014) with the following modifications: 1.1 mg / ml BSA lysis buffer ( Thermo Fisher Scientific, Waltham, MA, USA); 2. 200 pg of cDNA was used with a 1 / 5 reaction volume of the Illumina Nextera XT kit (Illumina, San Diego, CA, USA). The length distribution of the cDNA library was monitored using a DNA high-sensitivity kit on a Perkin Elmer Labchip (Perkin Elmer, Waltham, MA, USA). All samples were sequenced on an Illumina HiSeq 4000 system (Illumina, San Diego, CA, USA) using a 2 x 151-cycle indexed paired-end sequencing run (250 samples / channel).

[0158] For total mRNA bulk RNA-seq analysis, based on Figure 4 A gating strategy for BM Ly6C - GMP, proNeu1, proNeu2, and cMoP were sorted. PreNeu was sorted as described above. The Arcturus PicoPure RNA Isolation Kit (Applied Biosystems) was used according to the manufacturer's protocol. TMTotal RNA was extracted from Thermo Fisher Scientific. All human RNA was analyzed on an Agilent Bioanalyser for quality assessment, with a median RNA integrity number (RIN) of 9.4. Using the SMARTSeq v2 protocol (Picelli et al., 2014), 2 ng of total RNA and 1 μl of a 1:50,000 diluted ERCC RNA Spike-In control ( cDNA libraries were prepared using a Thermo Fisher Scientific (Illumina, San Diego, CA, USA) with the following modifications: 1. 20 μM TSO was added; 2. 200 pg cDNA was used with the Illumina Nextera XT reagent kit (Illumina, San Diego, CA, USA) with a 1 / 5 reaction volume. The length distribution of the cDNA library was monitored on a Perkin Elmer Labchip (Perkin Elmer, Waltham, MA, USA) using a DNA high sensitivity kit. All samples were run on an Illumina HiSeq 4000 system (Illumina) with 2 x 151 cycles of indexed double-end sequencing (26 samples / channel). The original reads were aligned to the GRCm38 constructed from the mouse genome using the STAR aligner. The read counts for each gene were then calculated using featureCounts (part of the Subread software package) based on the GENCODE gene annotation file version M20. Normalization was performed using log2-transformed read counts per kilobase per million mapped reads (log2RPKM) to account for transcript length and total number of reads. Differentially expressed genes (DEGs) were analyzed using edgeR (Robinson et al., 2009) only for protein-coding genes. DEGs with an FDR (false discovery rate) of less than 0.05 were selected as statistically significant.

[0159] In vitro cell culture

[0160] The sorted cells (1x10 4 ) were plated in 96-well plates in triplicate and cultured in StemSpan TMSFEM II medium (Stem Cell Technologies) containing penicillin (100 U / ml), streptomycin (100 μg / ml), a cytokine combination (50 ng / ml SCF, 10 ng / ml LIF, 20 ng / ml IL-3, 20 ng / ml IL-6), and with or without 50 ng / ml CSF-1. For human fetal bone marrow culture, frozen samples were thawed and sorted, and then cultured in StemSpan TM The cells were plated in 96-well plates in SFEM II medium containing penicillin (100 U / ml), streptomycin (100 μg / ml) and StemSpan TM Myeloid Expansion Supplement (Stem Cell Technologies). For colony assays, sorted cells (3x10 4 ) were cultured in Iscove's modified Dulbecco's medium (Sigma) containing 25 mM HEPES and L-glutamine (Chemtron), 10% (vol / vol) FBS, 1 mM sodium pyruvate, penicillin (100 U / ml) and streptomycin (100 μg / ml), 1% (wt / vol) methylcellulose (MethoCult M3234, Stem Cell Technologies), and the same cytokine combination as above. Representative colony images were collected using an Olympus IX-81 microscope (Olympus). Image brightness was adjusted using Photoshop.

[0161] adoptive cell transfer

[0162] As previously described (Chong et al., 2016), sorted uGFP + proNeu1、RFP + (Rosa26 mT / mG )proNeu2 and CD45.1 preNeu (5x10 each) 4 Intra-BM transfer of 100 cells (100 cells) into wild-type recipients was performed. Briefly, recipient mice were anesthetized with ketamine (150 mg / kg) / xylazine (10 mg / kg), and the fur of the right hind limb was shaved to expose the kneecap. Sorted proNeu1, proNeu2, and preNeu were mixed in equal proportions and resuspended in 1X PBS, and a volume of 10 μL was administered into the tibia through the kneecap using a 29-gauge insulin needle. For single population transfer, sorted uGFP was used. + Ly6C -GMP, proNeu1 and proNeu2 (5x10 4 24 or 72 hours after cell transfer, tibiae were harvested, stained, and analyzed by flow cytometry.

[0163] CLP-induced sepsis

[0164] As previously described (Rittirsch et al., 2009), cecal ligation and puncture were performed. Briefly, the abdominal cavity was exposed and the cecum was removed from the abdomen under ketamine / xylazine anesthesia. 50% (intermediate) or 80% (advanced) of the cecum was ligated distally at the ileocecal valve using a non-absorbable 7-0 suture. A 26-gauge needle was used to perforate the distal cecum, and a small drop of feces was squeezed through the puncture hole, and the cecum was then returned to the abdominal cavity. The peritoneum was closed, and the mice were subsequently treated with saline and buprenorphine (5-20 mg / kg) by subcutaneous injection. For sham-treated controls, the peritoneum was exposed and the cecum was removed from the abdomen as described above, and the peritoneum was then closed. Mice were euthanized and collected 2 weeks after surgery.

[0165] Quantitative and statistical analysis

[0166] Statistical analysis was performed using Prism software (Graphpad). Student's t-test or one-way analysis of variance (ANOVA) with Bonferroni correction was performed.

[0167] sheet

[0168] Table 1. (with Figure 2 Related) in mouse BM LEGENDScreen TM List of surface markers tested in

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[0179] Table 2 (related to Figure 2 PhenoGraph clustered cell type identification

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[0182] Table 3 (related to Figure 7 List of surface markers tested in human cord blood LEGENDScreen TM

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[0192] Table 4. Key Resources Table

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[0223] Yamanaka, R., Barlow, C, Lekstrom-Himes, J., Castilla, L. H., Liu, P. P., Eckhaus, M., Decker, T., Wynshaw-Boris, A., and Xanthopoulos, K. G. (1997). Impaired granulopoiesis, myelodysplasia, and early lethality in CCAAT / enhancer binding protein ε-deficient mice. Proc. Natl. Acad. Sci. U.S.A. 94, 13187-13192.

[0224] Those skilled in the art will appreciate that other changes and / or modifications can be suggested by those skilled in the art, and it is intended that the appended claims cover all such changes and modifications as fall within the spirit or scope of the disclosure as broadly described. For example, in the description herein, features of different example embodiments can be mixed, combined, interchanged, merged, adopted, modified, included, etc. between different example embodiments, or similar operations. Thus, the embodiments herein should be considered in all respects as illustrative and not restrictive.

Claims

1. A method of identifying a neutrophil progenitor cell, the method comprising: determining expression of at least one biomarker in a cell, the biomarker selected from the group consisting of CD71, LOX-1, CD164, CD112, CD181, TACSTD2, CD1 lb and CD49d; when it is determined that the cell has CD71 高 / + an expression profile and has at least one of the following expression profiles: LOX-1 中 / 低 / - , CD164 高 / + , CD112 高 / + , CD181 中 / 低 / - , TACSTD2 高 / + , CD11b 低 / - and / or CD49d 中 / 高 / + , wherein when the cell is identified as a neutrophil progenitor cell, the method further comprises: determining expression of the additional biomarker CD49d and side scatter (SSC) properties of the neutrophil progenitor cell; and identifying a subtype of the neutrophil progenitor cell based on the expression of the additional biomarker and / or side scatter properties, Wherein when the neutrophil progenitor cells are identified as CD49d 高 / + and SSC 低 , the neutrophil progenitor cell is identified as an early committed neutrophil progenitor cell, and wherein when said neutrophil progenitor cell is determined to be CD49d 中 / 低 / - and SSC 高 then said neutrophil progenitor cell is identified as an intermediate neutrophil progenitor cell, which is downstream of said early committed neutrophil progenitor cell in the neutrophil lineage.

2. The method of claim 1, wherein determining expression of the at least one biomarker and / or the additional biomarker comprises contacting the cell with one or more antibodies against the biomarker and / or the additional biomarker, wherein the at least one biomarker is selected from the markers in a cell consisting of CD71, LOX-1, CD164, CD112, CD181, TACSTD2, CD1 lb and CD49d; and wherein the additional biomarker is CD49d.

3. A method of sorting and / or separating early neutrophil progenitor cells from a population of cells, the method comprising: cells having CD71 高 / + , CD49d 中 / 高 / + , SSC 低 and having at least one of the following expression profiles: LOX-1 中 / 低 / - , CD164 高 / + , CD112 高 / + , CD181 中 / 低 / - , TACSTD2 高 / + , and / or CD11b 低 / - .

4. The method of claim 3, wherein the population of cells is derived from umbilical cord blood and / or bone marrow.

5. The method of claim 3 or 4, further comprising culturing the neutrophil progenitor cells to obtain proliferation and / or differentiation of the neutrophil progenitor cells, thereby obtaining progeny thereof.

6. The method of claim 3, wherein the selecting step comprises contacting the cells with antibodies against CD71 and CD49d and with one or more antibodies against one or more of LOX-1, CD164, CD112, CD181, TACSTD2 and CD1 lb.

7. A composition enriched for neutrophil progenitor cells having CD71 高 / + , CD49d 中 / 高 / + , SSC 低 and at least one of the following expression profiles: LOX-1 中 / 低 / - , CD164 高 / + , CD112 高 / + , CD181 中 / 低 / - , TACSTD2 高 / + , and / or CD11b 低 / - .

8. The composition of claim 7, for use in therapy.

9. The composition of claim 7, for use in treating neutropenia.

10. Use of the composition of claim 7 in the manufacture of a medicament for treating neutropenia.

11. A method of preparing an infusion composition, the method comprising: compositions enriched for neutrophil progenitor cells having CD71 高 / + , CD49d 中 / 高 / + , SSC 低 and at least one of the following expression profiles: LOX-1 中 / 低 / - , CD164 高 / + , CD112 高 / + , CD181 中 / 低 / - , TACSTD2 高 / + , and / or CD11b 低 / - .

12. The method of claim 11, wherein the enriching step comprises contacting the composition with antibodies against CD71, CD49d and with one or more antibodies against one or more of LOX-1, CD164, CD112, CD181, TACSTD2 and CD1 lb.

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