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> Anatomy > Tissue > Bone Marrow

Bone Marrow

Bone marrow is the soft, spongy tissue found inside bones that produces blood cells.
It plays a crucial role in hematopoiesis, the process of blood cell formation.
Bone marrow contains stem cells that can differentiate into various blood cell types, including red blood cells, white blood cells, and platelets.
Understanding bone marrow biology is essential for the study and treatment of hematological disorders, such as leukemia, anemia, and immune system dysfunction.
Researchers can leverage PubCompare.ai's AI-driven protocol comparison tool to optimize their bone marrow research, enhancing expereiements and improving reproducibility and accuracy.

Most cited protocols related to «Bone Marrow»

1
Comprehensive Cell Line RNA Isolation
Thirty-four neuroblastoma cell lines were grown to subconfluency according to standard culture conditions. RNA was isolated using the RNeasy Midi Kit (Qiagen) according to the manufacturer's instructions. Nine RNA samples from pooled normal human tissues (heart, brain, fetal brain, lung, trachea, kidney, mammary gland, small intestine and uterus) were obtained from Clontech. Blood and fibroblast biopsies were obtained from different normal healthy individuals. Thirteen leukocyte samples were isolated from 5 ml fresh blood using Qiagen's erythrocyte lysis buffer. Fibroblast cells from 20 upper-arm skin biopsies were cultured for a short time (3-4 passages) and harvested at subconfluency as described [22 (link)]. Bone marrow samples were obtained from nine patients with no hematological malignancy. Total RNA of leukocyte, fibroblast and bone marrow samples was extracted using Trizol (Invitrogen), according to the manufacturer's instructions.
Vandesompele J., De Preter K., Pattyn F., Poppe B., Van Roy N., De Paepe A, & Speleman F. (2002). Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology, 3(7), research0034.1-research0034.11.
Publication 2002
Arm, Upper Biopsy BLOOD Bone Marrow Brain Buffers Cell Lines Erythrocytes Fetus Fibroblasts Heart Hematologic Neoplasms Homo sapiens Intestines, Small Kidney Leukocytes Lung Mammary Gland Neuroblastoma Patients Skin Tissues Trachea trizol Uterus
2
Identifying CD69+ Regulatory Signatures in Bone Marrow

Protocol full text hidden due to copyright restrictions

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Publication 2019
Biological Processes Bone Marrow CD8-Positive T-Lymphocytes Cells Cellular Structures Gene Expression Genes Genes, vif Population Group Single-Cell RNA-Seq Tissue Donors
3
Systematic Evaluation of Trajectory Inference Algorithms
We undertook a systematic evaluation of the performance of Palantir in comparison to widely-used trajectory inference algorithms such as Monocle2, Diffusion Pseudotime (DPT), Partition based Graph Abstraction (PAGA- based on DPT), Slingshot, FateID, and Monocle 2.
We first compared the algorithms by evaluating their setup: the prior biology knowledge required as input and the diversity of outputs provided by each algorithm using the following criteria:
Supplementary Fig. 17a summarizes the characteristics of the different algorithms according to the criteria outline above:
Thus, Palantir uses minimal a priori biological information to (a) automatically determine the different terminal states, (b) generate a unified pseudo-time ordering to compare gene expression trends across lineages and (c) identify continuous branch probabilities and differentiation potential for each cell.
We next used the CD34+ human bone marrow data (replicate 1) as a benchmark to compare the results of the different algorithms. Due to the varied nature of the different outputs, we evaluated the ability of the algorithm to determine known and well established features of human hematopoiesis such as (a) identification of the different lineages represented in the data, with emphasis on less frequent populations such as megakaryocytes, cDCs and pDCs, which are more subtle and challenging to infer (b) recovering known expression trends of key genes across multiple lineages. We choose well-studied canonical genes across the different lineages, whose expression dynamics are known and can thus serve as ground truth. The following canonical genes, representing a broad spectrum of gene expression dynamics, were chosen for this evaluation:
Supplementary Fig. 17b shows the results of this comparison for the different algorithms. Palantir and DPT were able to identify the megakaryocyte lineages, whereas PAGA and Slingshot included these cells to be part of the erythroid lineage. Palantir was the only algorithm able to recover the distinction between the two DC lineages. Comparing the expression trends, all algorithms except Monocle 2 recovered the downregulation of CD34 across all lineages. Palantir recovers the known gene expression trends across all lineages (Fig. 2). While PAGA, DPT and Slingshot identify the trends in the larger lineages, PAGA (and DPT) suffer from a loss in resolution in gene expression trends and Slingshot does not provide a unified ordering of cells to compare gene expression trends across lineages. FateID with the default clustering using RaceID failed to identify any correct lineages and gene expression trends, whereas FateID with a preprocessing procedure and clustering followed in Palantir identifies correct expression trends in only the monocyte and CLP lineages. Monocle 2 could not recover the key hematopoietic lineages or expression trends from the CD34+ bone marrow data. See Supplementary Note 6 for a detailed description of the different algorithms and their performance.
Setty M., Kiseliovas V., Levine J., Gayoso A., Mazutis L, & Pe’er D. (2019). Characterization of cell fate probabilities in single-cell data with Palantir. Nature biotechnology, 37(4), 451-460.
Publication 2019
Biopharmaceuticals Bone Marrow Cells chenodeoxycholate sulfate conjugate Differentiations, Cell Diffusion DNA Replication Down-Regulation Gene Expression Genes Hematopoiesis Hematopoietic System Homo sapiens Megakaryocytes Monocytes PDC protein, human
4
Whole-Genome Sequencing of Multiple Myeloma
Informed consent from MM patients was obtained in line with the Declaration of Helsinki. DNA was extracted from bone marrow aspirate (tumor) and blood (normal). WGS libraries (370-410 bp inserts) and WES libraries (200-350 bp inserts) were constructed and sequenced on an Illumina GA-II sequencer using 101 and 76 bp paired-end reads, respectively. Sequencing reads were procesed with the Firehose pipeline, identifying somatic point mutations, indels, and other structural chromosomal rearrangements. Structural rearrangements affecting protein-coding regions were then subjected to manual review to exclude alignment artifacts. True positive mutation rates were estimated by Sequenom mass spectrometry genotyping of randomly selected mutations. HOXA9 shRNAs were introduced into MM cell lines using lentiviral infection using standard methods.
A complete description of the materials and methods are provided in the Supplementary Information.
Chapman M.A., Lawrence M.S., Keats J.J., Cibulskis K., Sougnez C., Schinzel A.C., Harview C.L., Brunet J.P., Ahmann G.J., Adli M., Anderson K.C., Ardlie K.G., Auclair D., Baker A., Bergsagel P.L., Bernstein B.E., Drier Y., Fonseca R., Gabriel S.B., Hofmeister C.C., Jagannath S., Jakubowiak A.J., Krishnan A., Levy J., Liefeld T., Lonial S., Mahan S., Mfuko B., Monti S., Perkins L.M., Onofrio R., Pugh T.J., Vincent Rajkumar S., Ramos A.H., Siegel D.S., Sivachenko A., Trudel S., Vij R., Voet D., Winckler W., Zimmerman T., Carpten J., Trent J., Hahn W.C., Garraway L.A., Meyerson M., Lander E.S., Getz G, & Golub T.R. (2011). Initial genome sequencing and analysis of multiple myeloma. Nature, 471(7339), 467-472.
Publication 2011
BLOOD Bone Marrow Cell Lines Chromosomes Diploid Cell Gene Rearrangement HOXA9 protein, human INDEL Mutation Infection Mass Spectrometry Multiple Acyl Coenzyme A Dehydrogenase Deficiency Mutation Neoplasms Open Reading Frames Patients Point Mutation Short Hairpin RNA
5
Whole-Genome Sequencing of Multiple Myeloma
Informed consent from MM patients was obtained in line with the Declaration of Helsinki. DNA was extracted from bone marrow aspirate (tumor) and blood (normal). WGS libraries (370-410 bp inserts) and WES libraries (200-350 bp inserts) were constructed and sequenced on an Illumina GA-II sequencer using 101 and 76 bp paired-end reads, respectively. Sequencing reads were procesed with the Firehose pipeline, identifying somatic point mutations, indels, and other structural chromosomal rearrangements. Structural rearrangements affecting protein-coding regions were then subjected to manual review to exclude alignment artifacts. True positive mutation rates were estimated by Sequenom mass spectrometry genotyping of randomly selected mutations. HOXA9 shRNAs were introduced into MM cell lines using lentiviral infection using standard methods.
A complete description of the materials and methods are provided in the Supplementary Information.
Chapman M.A., Lawrence M.S., Keats J.J., Cibulskis K., Sougnez C., Schinzel A.C., Harview C.L., Brunet J.P., Ahmann G.J., Adli M., Anderson K.C., Ardlie K.G., Auclair D., Baker A., Bergsagel P.L., Bernstein B.E., Drier Y., Fonseca R., Gabriel S.B., Hofmeister C.C., Jagannath S., Jakubowiak A.J., Krishnan A., Levy J., Liefeld T., Lonial S., Mahan S., Mfuko B., Monti S., Perkins L.M., Onofrio R., Pugh T.J., Vincent Rajkumar S., Ramos A.H., Siegel D.S., Sivachenko A., Trudel S., Vij R., Voet D., Winckler W., Zimmerman T., Carpten J., Trent J., Hahn W.C., Garraway L.A., Meyerson M., Lander E.S., Getz G, & Golub T.R. (2011). Initial genome sequencing and analysis of multiple myeloma. Nature, 471(7339), 467-472.
Publication 2011
BLOOD Bone Marrow Cell Lines Chromosomes Diploid Cell Gene Rearrangement HOXA9 protein, human INDEL Mutation Infection Mass Spectrometry Multiple Acyl Coenzyme A Dehydrogenase Deficiency Mutation Neoplasms Open Reading Frames Patients Point Mutation Short Hairpin RNA

Most recents protocols related to «Bone Marrow»

1
In Vivo Endogenous Cell Transduction
Not available on PMC !

Example 9

In a preferred embodiment, endogenous cells are transfected with vectors such as those described herein in vivo by introduction of the therapeutic vector(s) into the host blood, tissues, nervous system, bone marrow, etc. The greatest benefit may be achieved by modifying a large number of endogenous target cells. This may be accomplished by using an appropriately-sized, catheter-like device, or needle to inject the therapeutic vector(s) into the venous or arterial circulation, into a specific tissue, such as muscle tissue, or into the nervous system. In a preferred embodiment, the virus is pseudotyped with VSV-G envelope glycoprotein and native HIV-1 env proteins.

US11859168B2. Electroporation, developmentally-activated cells, pluripotent-like cells, cell reprogramming and regenerative medicine (2024-01-02). None [US]. Inventors: Christopher B. Reid [US], Melissa Braga [US], Lilian N. Santamaria [US], Ashley N. Wickrema [US], Marinne D. Wickrema [US], Anthony Hao Dinh [US], Yaman Eksioglu [US], Mat Hoang Ho [US].
Patent 2024
Arteries BLOOD Bone Marrow Catheters Cells Cloning Vectors Gene Products, env Genetic Vectors Glycoproteins HIV-1 Medical Devices Muscle Tissue Needles Systems, Nervous Therapeutics Tissues Veins Virus
2
In Vivo Therapeutic Vector Delivery
Not available on PMC !

Example 25

In a preferred embodiment, blood stem/progenitor cells, and target cells are transfected with the therapeutic vector(s) (or associated therapeutic virus) in vivo by introduction of the therapeutic vector(s) into the host blood, tissues, or bone marrow, etc. The greatest benefit may be achieved by modifying a large number of endogenous target and stem/progenitor cells. This may be accomplished by using an appropriately-sized, catheter-like device, or needle to inject the therapeutic vector(s) into the venous or arterial circulation. In a preferred embodiment, the virus is pseudotyped with VSV-G envelope glycoprotein and native HIV-1 env proteins.

US11859168B2. Electroporation, developmentally-activated cells, pluripotent-like cells, cell reprogramming and regenerative medicine (2024-01-02). None [US]. Inventors: Christopher B. Reid [US], Melissa Braga [US], Lilian N. Santamaria [US], Ashley N. Wickrema [US], Marinne D. Wickrema [US], Anthony Hao Dinh [US], Yaman Eksioglu [US], Mat Hoang Ho [US].
Patent 2024
Arteries BLOOD Bone Marrow Catheters Cells Cloning Vectors Gene Products, env Glycoproteins HIV-1 Medical Devices Needles Peripheral Blood Stem Cells Satellite Viruses Stem Cells Therapeutics Tissues Veins Virus
3
Immunosuppressive Tumor-Derived MSCs
Not available on PMC !

Example 7

Tumor-Derived MSC-Like Lymphoma Stromal Cells are Immunosuppressive

Since the tumor cells in lymphoma are not adherent, it is possible to isolate tumor stromal cells from lymphomas developed in p53+/− mice. It was observed that these cells can be passaged in vitro and can be differentiated into adipocytes and osteoblast-like cells. Interestingly, like bone marrow derived MSCs, these tumor stromal cells are also immunosuppressive and can effectively inhibit the proliferation of ant-CD3-activated splenocytes. This immunosuppressive effect was also dependent on IFNγ+TNF α and NO, since anti-IFNγ IFNγ and iNOS inhibitors could reverse the immunosuppressive effect.

US11872250B2. Methods for inducing an immune response by administering activated mesenchymal stem cells (2024-01-16). Rutgers, The State University of New Jersey [US]. Inventors: Yufang Shi [US], Guangwen Ren [US], Liying Zhang [US].
Patent 2024
Adipocytes Bone Marrow Cardiac Arrest Cells Immunosuppressive Agents inhibitors Interferon Type II Lymphoma Mesenchyma Mus Neoplasms NOS2A protein, human Osteoblasts Response, Immune Stem, Plant Stromal Cells Tumor-Derived Activated Cells Tumor Necrosis Factor-alpha
4
Transfection of Hematopoietic Cells
Not available on PMC !

Example 26

Blood cells, such as mature peripheral blood T lymphocytes, monocytes, macrophages, T cell progenitors, macrophage-monocyte progenitor cells, and/or pluripotent hematopoietic stem cells (such as those found in umbilical cord blood and occupying bone marrow spaces) as well as other stem/progenitor cells can be transfected using the therapeutic vector(s) in vitro. Appropriate concentrations of the therapeutic vector(s) may be those consistent with Browning et al., 1999. Subsequently, cells are expanded (propagated) in vitro, and are then transferred to the host via introduction of the cells to the venous or arterial circulation using an intravenous needle or catheter. Subsequently, cells transfected with the therapeutic vectors are able to “home” to the bone marrow and other tissues.

It is understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application and scope of the appended claims. All publications, patents, and patent applications cited herein are hereby incorporated by reference in their entirety for all purposes.

US11859168B2. Electroporation, developmentally-activated cells, pluripotent-like cells, cell reprogramming and regenerative medicine (2024-01-02). None [US]. Inventors: Christopher B. Reid [US], Melissa Braga [US], Lilian N. Santamaria [US], Ashley N. Wickrema [US], Marinne D. Wickrema [US], Anthony Hao Dinh [US], Yaman Eksioglu [US], Mat Hoang Ho [US].
Patent 2024
Arteries BLOOD Blood Cells Bone Marrow Catheters Cells Cloning Vectors Light Macrophage Monocytes Needles Stem Cells Stem Cells, Hematopoietic T-Lymphocyte Therapeutics Tissues Umbilical Cord Blood Veins
5
Automated Femur Segmentation and Joint Axis Extraction
3D Slicer 4.13 (Fedorov et al., 2012 (link)) was used to segment MRI images. Each femur was split into five parts similar to previous studies (Kainz et al., 2020 (link))—the proximal trabecular bone, the growth plate, the cortical bone of the shaft, the bone marrow and the distal trabecular bone. STL-files of all parts and additionally a file containing the full femur were exported. The STAPLE-Toolbox of Modenese and Renault (2021) (link) was used to identify the femoral head and the epicondyles representing the hip and the knee joint axis using the “GIBOC-Femur” and “GIBOC-Cylinder” algorithms, respectively. If “GIBOC-Cylinder” algorithm failed to fit a cylinder through both epicondyles, “GIBOC-Ellipsoids” algorithm was used to fit ellipsoids through medial and lateral epicondyles. The hip joint center and knee joint axis were required to transform the femur into the OpenSim coordinate system.
The diaphysis of the femur was defined by removing 20% off the top and bottom of the femur. Then, the principal inertia axis of the remaining part was calculated to identify the shaft axis. The neck axis was defined by fitting a least-squares cylinder through surface nodes of the femoral neck. The longitudinal axis of this cylinder was constrained to pass through the femoral head center. The AVA was calculated as the angle between the neck axis and the medial-lateral knee axis obtained from STAPLE-Toolbox (Modenese and Renault, 2021 (link)) in the transverse plane. The NSA was computed as the angle between the neck axis and shaft axis in 3D space.
Koller W., Gonçalves B., Baca A, & Kainz H. (2023). Intra- and inter-subject variability of femoral growth plate stresses in typically developing children and children with cerebral palsy. Frontiers in Bioengineering and Biotechnology, 11, 1140527.
Publication 2023
Bone Marrow Cancellous Bone Compact Bone Diaphyses Epiphyseal Cartilage Epistropheus Femur Femur Heads Hip Joint Knee Joint Neck Neck, Femur Staple, Surgical

Top products related to «Bone Marrow»

1
Fetal bovine serum (fbs) by Thermo Fisher Scientific
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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
2
Penicillin streptomycin by Thermo Fisher Scientific
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Penicillin/streptomycin is a commonly used antibiotic solution for cell culture applications. It contains a combination of penicillin and streptomycin, which are broad-spectrum antibiotics that inhibit the growth of both Gram-positive and Gram-negative bacteria.
3
Penicillin by Thermo Fisher Scientific
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Penicillin is a type of antibiotic used in laboratory settings. It is a broad-spectrum antimicrobial agent effective against a variety of bacteria. Penicillin functions by disrupting the bacterial cell wall, leading to cell death.
4
Dulbecco modified eagle medium (dmem) by Thermo Fisher Scientific
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DMEM (Dulbecco's Modified Eagle's Medium) is a cell culture medium formulated to support the growth and maintenance of a variety of cell types, including mammalian cells. It provides essential nutrients, amino acids, vitamins, and other components necessary for cell proliferation and survival in an in vitro environment.
5
Streptomycin by Thermo Fisher Scientific
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Streptomycin is a broad-spectrum antibiotic used in laboratory settings. It functions as a protein synthesis inhibitor, targeting the 30S subunit of bacterial ribosomes, which plays a crucial role in the translation of genetic information into proteins. Streptomycin is commonly used in microbiological research and applications that require selective inhibition of bacterial growth.
6
α mem by Thermo Fisher Scientific
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α-MEM is a cell culture medium formulated for the growth and maintenance of mammalian cells. It provides a balanced salt solution, amino acids, vitamins, and other nutrients required for cell proliferation.
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L glutamine by Thermo Fisher Scientific
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L-glutamine is an amino acid that is commonly used as a dietary supplement and in cell culture media. It serves as a source of nitrogen and supports cellular growth and metabolism.
8
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RPMI 1640 is a common cell culture medium used for the in vitro cultivation of a variety of cells, including human and animal cells. It provides a balanced salt solution and a source of essential nutrients and growth factors to support cell growth and proliferation.
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M-CSF is a recombinant human macrophage colony-stimulating factor (M-CSF) that promotes the proliferation and differentiation of macrophages from hematopoietic progenitor cells. It functions as a cytokine and is involved in the regulation of macrophage production and function.
10
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GM-CSF is a laboratory reagent used for cell culture applications. It is a recombinant human granulocyte-macrophage colony-stimulating factor that promotes the growth and differentiation of hematopoietic cells.

More about "Bone Marrow"

Bone marrow is the soft, spongy tissue located within the bones, playing a crucial role in hematopoiesis, the process of blood cell formation.
This vital tissue contains stem cells that can differentiate into various blood cell types, including erythrocytes (red blood cells), leukocytes (white blood cells), and thrombocytes (platelets).
Understanding the biology of bone marrow is essential for the study and treatment of hematological disorders, such as leukemia, anemia, and immune system dysfunctions.
Researchers can leverage the power of AI-driven tools like PubCompare.ai's protocol comparison feature to optimize their bone marrow research, enhancing experiments and improving reproducibility and accuracy.
When conducting bone marrow studies, it's important to consider the use of essential cell culture components like fetal bovine serum (FBS), penicillin/streptomycin, penicillin, DMEM (Dulbecco's Modified Eagle Medium), streptomycin, α-MEM (Alpha Minimum Essential Medium), L-glutamine, and RPMI 1640 (Roswell Park Memorial Institute medium).
These supplements and media can play a crucial role in supporting the growth and differentiation of bone marrow-derived cells.
Additionally, the use of growth factors like M-CSF (Macrophage Colony-Stimulating Factor) and GM-CSF (Granulocyte-Macrophage Colony-Stimulating Factor) can be beneficial in promoting the proliferation and maturation of specific blood cell lineages derived from bone marrow stem cells.
By leveraging the insights and tools provided by PubCompare.ai, researchers can optimize their bone marrow experiments, leading to more reproducible and accurate results that advance the understanding and treatment of hematological conditions.