Tissue cytometry

Last updated

Tissue image cytometry or tissue cytometry is a method of digital histopathology and combines classical digital pathology (glass slides scanning and virtual slide generation) and computational pathology (digital analysis) into one integrated approach with solutions for all kinds of diseases, tissue and cell types as well as molecular markers and corresponding staining methods to visualize these markers. Tissue cytometry uses virtual slides as they can be generated by multiple, commercially available slide scanners, as well as dedicated image analysis software – preferentially including machine and deep learning algorithms. [1] Tissue cytometry enables cellular analysis within thick tissues, retaining morphological and contextual information, including spatial information on defined cellular subpopulations. [2]

Contents

Phenotypic and functional analysis of molecules, cells, morphological tissue entities within their native tissue environment using digitized slides; it includes the measurement of areas, intensities, percentages and multiple other parameters. Picture created using StrataQuest Image Analysis Platform by TissueGnsotics. Tissue Cytometry.png
Phenotypic and functional analysis of molecules, cells, morphological tissue entities within their native tissue environment using digitized slides; it includes the measurement of areas, intensities, percentages and multiple other parameters. Picture created using StrataQuest Image Analysis Platform by TissueGnsotics.

In this process, a tissue sample, either formalin-fixed paraffin-embedded (FFPE) or frozen tissue section, also referred to as “cryocut”, is labelled with either immunohistochemistry [3] (IHC) or immunofluorescent markers, scanned with high-throughput slide scanners and the data gathered from virtual slides is processed and analyzed using software that is able to identify individual cells in tissue context automatically and distinguish between nucleus and cytoplasm for each cell. [1] Additional algorithms can identify cellular membranes, subcellular structures (like cytoskeletal fibers, vacuoles, nucleoli) and/or multicellular tissue structures (glands, glomeruli, epidermis, or tumor foci). [4] Fluorescence Activated Cell Sorting (FACS) is a method of analysis that measures fluorescence signals on single cells, where the signal comes from antibody-mediated staining techniques and phenotypes detected by flow cytometry. [5] The major limitation of flow cytometry is that it can only be applied – as the name suggest – to cells in solution. Although methods of “solubilizing” solid tissue exist, any such processing irrevocably destroys the tissue architecture and any spatial context. Hence, tissue cytometry complements the use of flow cytometry and fluorescence microscope [6] in basic research, clinical practice, and clinical trials by providing FACS-like analyses on solid tissue sections (as well as adherent cell cultures) in situ. The advantage of tissue cytometry against flow cytometry is that tissue cytometry does not require the cells to be suspended in fluid, aiding in maintaining the integrity of the tissue structure, morphology, and contextual information, further assisting in precise and accurate contextual analysis that are not possible in flow cytometry.

History

Immunohistochemistry is used in clinical practice, where tissue biopsies from every potential cancer patient are collected, fixed in formalin and embedded on paraffin. These tissue sections are serially cut in a microtome to provide thin sections, representing the diagnostic material for clinical diagnoses. [3] Once stained initially with hematoxylin and eosin stain to detect cancer cells. Multiple marker staining is performed for proliferation, lineage, prognostic and oncogenic targets. Pathologists used optical microscope for the evaluation through the objective lenses and conclude the diagnosis by scoring the staining in percentage or as positive/negative. Visual evaluation provides a subjective diagnosis and plan of treatment.

By converting glass slides into digital images, digital pathology changed how pathologists interacted with tissue specimens. However, the initial phase of digital pathology primarily focused on image viewing and sharing. While this enabled remote consultations and facilitated image archiving, it did not fundamentally alter the core process of pathology: the manual interpretation of tissue morphology by human experts.

A more robust and automated system was designed to perform flow cytometry-like analyses on immunostained cells in a fixed tissue and termed tissue cytometry. [7] The technique was introduced in the 1990s based on patents by Steiner and Ecker, [8] describing a procedure for “Cytometric Analysis of Diverse Cell Populations in Tissue Sections or Cell Culture Visualized Through Fluorescence Dyes and/or Chromogens". Tissue cytometry emerged as a transformative extension of digital pathology, promising to bridge the gap between image-based analysis and quantitative, data-driven insights. At its core, tissue cytometry enables the automated and quantitative analysis of cellular and tissue features. By employing computational algorithms and machine learning models, it can accurately segment nuclei, identify cell types, and quantify protein expression levels within the tissue context.

Additional patents were filed in the early 21st century by Hernani et al. to perform virtual flow cytometry on immunostained tissue. [9] The latter's basics were derived from the procedure presented in 1982 by Gillete et al., describing the qualitative analysis of spectral mixtures by using factor analysis in conjunction with a spectral reference library. [10] Following this study, Zhou R et al. published a method to quantify prostate-specific acid phosphatase (PSAP) in histologic sections of prostate tumor with the peroxidase-antiperoxidase (PAP) complex technique using diaminobenzidine (DAB) as a substrate. [11]

The integration of AI and machine learning has been instrumental in the development of tissue cytometry. For instance, AI-driven algorithms can be trained to identify specific cell types, detect subtle morphological changes associated with disease, or quantify the density of immune cells within a tumor microenvironment. [12]

By precisely delineating individual nuclei, researchers can extract valuable information about nuclear size, shape, and texture, which can be correlated with various pathological conditions. Similarly, tissue segmentation algorithms enable the identification of different tissue compartments, such as tumor, stroma, and immune infiltrate, facilitating the analysis of spatial relationships between cellular components. [13]

Tissue cytometry Environment/ Tissue Cytometers

Modern tissue cytometers can analyze many thousands of cells within the tissue sample in "real time".

A tissue cytometer has 2 main components: (I) a high-throughput scanner to acquire the high-quality virtual image of immunohistochemical and/or fluorescent marker labelled tissue sections, (II) software for image analysis and data interpretation.

Applications of Tissue cytometry

Tissue cytometry assists in performing phenotypic characterization of cellular sub-populations in spatial context. Image created by StrataQuest by TissueGnostics Quantifying Tumour Microenvironment.png
Tissue cytometry assists in performing phenotypic characterization of cellular sub-populations in spatial context. Image created by StrataQuest by TissueGnostics

Tumor Microenvironment: Tissue cytometry is heavily used in research to characterize the tumor microenvironment including e.g. identification of the immune landscape or tumor-vascularization, within IHC/IF-processed tissue sections. One reason is that by using this technology the complex tissue architecture stays intact and therefore also spatial relationships between cellular phenotypes and/or multicellular structures can be analyzed. [14]

By utilizing tissue cytometry multiple research groups were able to demonstrate the impact of various immune cell subpopulations (CD4, CD68, CD8, CD20, Foxp3, PD1) on patient survival in different cancer types (e.g. breast cancer, colon cancer, gastric cancer, melanoma, non-small cell lung cancer). [14] Since in cancer therapy a novel treatment strategy is targeting immune checkpoints (molecules that inhibit the antitumoral immune reaction), the insights gained by tissue cytometry may help to find new target molecules/biomarkers as well as to determine the best treatment strategy for patients. [14]

StrataQuest by TissueGnostics assists in immunophenotyping by determining the immune status of organs/tissues in-situ. Advance analysis can also be performed including cell-cell interaction, distance zone mapping, defining macrostructures, etc. Immunophenotyping.png
StrataQuest by TissueGnostics assists in immunophenotyping by determining the immune status of organs/tissues in-situ. Advance analysis can also be performed including cell-cell interaction, distance zone mapping, defining macrostructures, etc.

Immunology: Immune cell context is important for delineating the etymology of inflammatory diseases, which often result from impaired function of adaptive and/or innate immune cells. Tissue cytometry is useful for detecting and localizing specific cells, especially heterogeneous populations, within their native tissue environment and identifying the cues behind the disease. [15]   For example, it was used to investigate IgG4-related diseases: one paper reports about fibrosing mediastinitis being driven by CD4+ CTLs rather than Th2 cells where infiltration of CD4+ CTLs was illustrated by tissue cytometry. [16] Follow-up studies investigated how follicular T cells influence B-cell class-switching events in IgG4-related disease and Kimura disease – researchers found a correlation between AICDA+CD19+ B cells and IgG4 expression using tissue cytometry. [17]

Mesenchymal Stem Cells Characterization: Mesenchymal stem cells (MSCs) are multipotent cells that have the capacity differentiate into several sub-types such as bone, cartilage, muscle, developing teeth and fat tissue which has clinical importance for regenerative medicine. [18] However, although there are defined minimal phenotypic criteria, MSCs due to their heterogeneous nature need to be further characterized regarding their distinct biomarkers. [19] Tissue cytometry promisingly assists to describe the biomarkers of quiescent MCSs and furthermore characterize the effect of hyaluronan on this population. [20] Tissue cytometry can also used to investigate MSCs interaction with glioblastoma: to characterize cell fusion, extracellular vesicle transfer and intercellular communications. [21] Additionally, tissue cytometry is utilized to image the murine hippocampus and visualize M1/M2 microglia in mice with MSCs transplantation as a model for Alzheimer’s disease. [22]

Tissue cytometry provides applications to quantify cellular pathogens including intracellular parasites (e.g. leishmania) and viral load (e.g. SARS-CoV-2, Influenza, HIV, Zika, Dengue, Hepatitis, HSV, Chikungunya). Output image of tissue analysis performed on StrataQuest by TissueGnostics Qunatification of Cellular Pathogens.png
Tissue cytometry provides applications to quantify cellular pathogens including intracellular parasites (e.g. leishmania) and viral load (e.g. SARS-CoV-2, Influenza, HIV, Zika, Dengue, Hepatitis, HSV, Chikungunya). Output image of tissue analysis performed on StrataQuest by TissueGnostics

COVID-19: COVID-19 pandemic required various tools to outline the disease progression and severity. Using tissue cytometry, researchers reported about interplay of immune cells and SARS-CoV-2 virus and its effect on disease: for instance, one study showed that CD4+ cytotoxic T cells expanded significantly in the lungs in severe COVID-19. [23] Another finding illustrates loss of germinal centers in lymph nodes and spleens in acute COVID-19, which was shown by multi-color immunofluorescence cytometry. [24]

Neuroscience: Tracking neurodevelopmental processes is an active field of research in neuroscience. Quantitative tissue analysis is widely employed in the field to determine the role of different stimuli in the nervous system. [25] [26] [27] [28] A research group reported about the effect of the magnetic field on neural differentiation of pluripotent stem cells, where the phenotypic effects were observed using tissue cytometry. [25] Another application of tissue cytometry in neuroscience was shown in a study designed to evaluate the effect of stress on hypothalamic neurons. [26]

Related Research Articles

<span class="mw-page-title-main">Bone marrow</span> Semi-solid tissue in the spongy portions of bones

Bone marrow is a semi-solid tissue found within the spongy portions of bones. In birds and mammals, bone marrow is the primary site of new blood cell production. It is composed of hematopoietic cells, marrow adipose tissue, and supportive stromal cells. In adult humans, bone marrow is primarily located in the ribs, vertebrae, sternum, and bones of the pelvis. Bone marrow comprises approximately 5% of total body mass in healthy adult humans, such that a man weighing 73 kg (161 lbs) will have around 3.7 kg (8 lbs) of bone marrow.

<span class="mw-page-title-main">Lymphocyte</span> Subtype of white blood cell

A lymphocyte is a type of white blood cell (leukocyte) in the immune system of most vertebrates. Lymphocytes include T cells, B cells, and innate lymphoid cells, of which natural killer cells are an important subtype. They are the main type of cell found in lymph, which prompted the name "lymphocyte". Lymphocytes make up between 18% and 42% of circulating white blood cells.

<span class="mw-page-title-main">Flow cytometry</span> Lab technique in biology and chemistry

Flow cytometry (FC) is a technique used to detect and measure the physical and chemical characteristics of a population of cells or particles.

<span class="mw-page-title-main">Immunostaining</span> Biochemical technique

In biochemistry, immunostaining is any use of an antibody-based method to detect a specific protein in a sample. The term "immunostaining" was originally used to refer to the immunohistochemical staining of tissue sections, as first described by Albert Coons in 1941. However, immunostaining now encompasses a broad range of techniques used in histology, cell biology, and molecular biology that use antibody-based staining methods.

<span class="mw-page-title-main">Immunohistochemistry</span> Common application of immunostaining

Immunohistochemistry is a form of immunostaining. It involves the process of selectively identifying antigens (proteins) in cells and tissue, by exploiting the principle of antibodies binding specifically to antigens in biological tissues. Albert Hewett Coons, Ernest Berliner, Norman Jones and Hugh J Creech was the first to develop immunofluorescence in 1941. This led to the later development of immunohistochemistry.

<span class="mw-page-title-main">Hybridoma technology</span> Method for producing lots of identical antibodies

Hybridoma technology is a method for producing large numbers of identical antibodies, also called monoclonal antibodies. This process starts by injecting a mouse with an antigen that provokes an immune response. A type of white blood cell, the B cell, produces antibodies that bind to the injected antigen. These antibody producing B-cells are then harvested from the mouse and, in turn, fused with immortal myeloma cancer cells, to produce a hybrid cell line called a hybridoma, which has both the antibody-producing ability of the B-cell and the longevity and reproductivity of the myeloma.

Stromal cells, or mesenchymal stromal cells, are differentiating cells found in abundance within bone marrow but can also be seen all around the body. Stromal cells can become connective tissue cells of any organ, for example in the uterine mucosa (endometrium), prostate, bone marrow, lymph node and the ovary. They are cells that support the function of the parenchymal cells of that organ. The most common stromal cells include fibroblasts and pericytes. The term stromal comes from Latin stromat-, "bed covering", and Ancient Greek στρῶμα, strôma, "bed".

<span class="mw-page-title-main">Exosome (vesicle)</span> Membrane-bound extracellular vesicles

Exosomes, ranging in size from 30 to 150 nanometers, are membrane-bound extracellular vesicles (EVs) that are produced in the endosomal compartment of most eukaryotic cells. In multicellular organisms, exosomes and other EVs are found in biological fluids including saliva, blood, urine and cerebrospinal fluid. EVs have specialized functions in physiological processes, from coagulation and waste management to intercellular communication.

Flow-FISH is a cytogenetic technique to quantify the copy number of RNA or specific repetitive elements in genomic DNA of whole cell populations via the combination of flow cytometry with cytogenetic fluorescent in situ hybridization staining protocols.

<span class="mw-page-title-main">Digital pathology</span>

Digital pathology is a sub-field of pathology that focuses on managing and analyzing information generated from digitized specimen slides. It utilizes computer-based technology and virtual microscopy to view, manage, share, and analyze digital slides on computer monitors. This field has applications in diagnostic medicine and aims to achieve more efficient and cost-effective diagnoses, prognoses, and disease predictions through advancements in machine learning and artificial intelligence in healthcare.

<span class="mw-page-title-main">Desmoplasia</span> Growth of fibrous or connective tissue

In medicine, desmoplasia is the growth of fibrous connective tissue. It is also called a desmoplastic reaction to emphasize that it is secondary to an insult. Desmoplasia may occur around a neoplasm, causing dense fibrosis around the tumor, or scar tissue (adhesions) within the abdomen after abdominal surgery.

<span class="mw-page-title-main">Intravital microscopy</span> Imaging of cells in living animals

Intravital microscopy is a form of microscopy that allows observing biological processes in live animals at a high resolution that makes distinguishing between individual cells of a tissue possible.

<span class="mw-page-title-main">The Hallmarks of Cancer</span> 2000 paper by Hanahan and Weinberg

The hallmarks of cancer were originally six biological capabilities acquired during the multistep development of human tumors and have since been increased to eight capabilities and two enabling capabilities. The idea was coined by Douglas Hanahan and Robert Weinberg in their paper "The Hallmarks of Cancer" published January 2000 in Cell.

<span class="mw-page-title-main">Tumor microenvironment</span> Surroundings of tumors including nearby cells and blood vessels

The tumor microenvironment is a complex ecosystem surrounding a tumor, composed of cancer cells, stromal tissue and the extracellular matrix. Mutual interaction between cancer cells and the different components of the tumor microenvironment support its growth and invasion in healthy tissues which correlates with tumor resistance to current treatments and poor prognosis. The tumor microenvironment is in constant change because of the tumor's ability to influence the microenvironment by releasing extracellular signals, promoting tumor angiogenesis and inducing peripheral immune tolerance, while the immune cells in the microenvironment can affect the growth and evolution of cancerous cells.

<span class="mw-page-title-main">Mass cytometry</span> Laboratory technique

Mass cytometry is a mass spectrometry technique based on inductively coupled plasma mass spectrometry and time of flight mass spectrometry used for the determination of the properties of cells (cytometry). In this approach, antibodies are conjugated with isotopically pure elements, and these antibodies are used to label cellular proteins. Cells are nebulized and sent through an argon plasma, which ionizes the metal-conjugated antibodies. The metal signals are then analyzed by a time-of-flight mass spectrometer. The approach overcomes limitations of spectral overlap in flow cytometry by utilizing discrete isotopes as a reporter system instead of traditional fluorophores which have broad emission spectra.

<span class="mw-page-title-main">Single-cell variability</span>

In cell biology, single-cell variability occurs when individual cells in an otherwise similar population differ in shape, size, position in the cell cycle, or molecular-level characteristics. Such differences can be detected using modern single-cell analysis techniques. Investigation of variability within a population of cells contributes to understanding of developmental and pathological processes,

A cancer-associated fibroblast (CAF) is a cell type within the tumor microenvironment that promotes tumorigenic features by initiating the remodelling of the extracellular matrix or by secreting cytokines. CAFs are a complex and abundant cell type within the tumour microenvironment; the number cannot decrease, as they are unable to undergo apoptosis.

Cell isolation is the process of separating individual living cells from a solid block of tissue or cell suspension. While some types of cell naturally exist in a separated form, other cell types that are found in solid tissue require specific techniques to separate them into individual cells. This may be performed by using enzymes to digest the proteins that binds these cells together within the extracellular matrix. After the matrix proteins have been digested, cells remain loosely bound together but can be gently separated mechanically. Following isolation, experiments can be performed on these single isolated cells including patch clamp electrophysiology, calcium fluorescence imaging, and immunocytochemistry. 

Breast cancer is the most prevalent type of cancer among women globally, with 685,000 deaths recorded worldwide in 2020. The most commonly used treatment methods for breast cancer include surgery, radiotherapy and chemotherapy. Some of these treated patients experience disease relapse and metastasis. The aggressive progression and recurrence of this disease has been attributed the presence of a subset of tumor cells known as breast cancer stem cells (BCSCs). These cells possess the abilities of self-renewal and tumor initiation, allowing them to be drivers of metastases and tumor growth. The microenvironment in which these cells reside is filled with residential inflammatory cells that provide the needed signaling cues for BCSC-mediated self-renewal and survival. The production of cytokines allows these cells to escape from the primary tumor and travel through the circulation to distant organs, commencing the process of metastasis. Due to their significant role in driving disease progression, BCSCs represent a new target by which to treat the tumor at the source of metastasis.

Ana Carrizosa Anderson is a Colombian-American microbiologist who is a professor at the Harvard Medical School. Her research combines transcriptomics and systems biology to understand T-cell response to chronic disease.

References

  1. 1 2 Cualing, Hernani D.; Zhong, Eric; Moscinski, Lynn (2006). ""Virtual flow cytometry" of immunostained lymphocytes on microscopic tissue slides:iHCFlow™ tissue cytometry". Cytometry Part B. 72B (1): 63–76. doi: 10.1002/cyto.b.20148 . ISSN   1552-4949. PMID   17133379. S2CID   36237785.
  2. ↑ Ferkowicz, Michael J.; Winfree, Seth; Sabo, Angela R.; Kamocka, Malgorzata M.; Khochare, Suraj; Barwinska, Daria; Eadon, Michael T.; Cheng, Ying-Hua; Phillips, Carrie L.; Sutton, Timothy A.; Kelly, Katherine J. (2021). "Large-scale, three-dimensional tissue cytometry of the human kidney: a complete and accessible pipeline". Laboratory Investigation. 101 (5): 661–676. doi:10.1038/s41374-020-00518-w. ISSN   1530-0307. PMC   8363780 . PMID   33408350.
  3. 1 2 Duraiyan, Jeyapradha; Govindarajan, Rajeshwar; Kaliyappan, Karunakaran; Palanisamy, Murugesan (August 2012). "Applications of immunohistochemistry". Journal of Pharmacy & Bioallied Sciences. 4 (Suppl 2): S307 –S309. doi: 10.4103/0975-7406.100281 . ISSN   0976-4879. PMC   3467869 . PMID   23066277.
  4. ↑ Imaging Modalities for Biological and Preclinical Research: A Compendium Volume One. IOP Publishing. 2021. pp. I.2.h-1-I.2.h-10.
  5. ↑ McKinnon, Katherine M. (2018-02-21). "Flow Cytometry: An Overview". Current Protocols in Immunology. 120: 5.1.1–5.1.11. doi:10.1002/cpim.40. ISSN   1934-3671. PMC   5939936 . PMID   29512141.
  6. ↑ Sanderson, Michael J.; Smith, Ian; Parker, Ian; Bootman, Martin D. (2014-10-01). "Fluorescence Microscopy". Cold Spring Harbor Protocols. 2014 (10): pdb.top071795. doi:10.1101/pdb.top071795. ISSN   1940-3402. PMC   4711767 . PMID   25275114.
  7. ↑ El-Achkar, Tarek M.; Winfree, Seth; Talukder, Niloy; Barwinska, Daria; Ferkowicz, Michael J.; Al Hasan, Mohammad (2022). "Tissue Cytometry With Machine Learning in Kidney: From Small Specimens to Big Data". Frontiers in Physiology. 13: 832457. doi: 10.3389/fphys.2022.832457 . ISSN   1664-042X. PMC   8931540 . PMID   35309077.
  8. ↑ USPTO.report. "Method and system for analyzing cells Patent Application". USPTO.report. Retrieved 2022-07-07.
  9. ↑ Cualing, Hernani D.; Zhong, Eric; Moscinski, Lynn (2007-01-15). ""Virtual flow cytometry" of immunostained lymphocytes on microscopic tissue slides: iHCFlow tissue cytometry". Cytometry Part B. 72 (1): 63–76. doi: 10.1002/cyto.b.20148 . ISSN   1552-4949. PMID   17133379. S2CID   36237785.
  10. ↑ Gillette, Paul C.; Lando, Jerome B.; Koenig, Jack L. (1983-04-01). "Factor analysis for separation of pure component spectra from mixture spectra". Analytical Chemistry. 55 (4): 630–633. doi:10.1021/ac00255a011. ISSN   0003-2700.
  11. ↑ Zhou, R.; Parker, D. L.; Hammond, E. H. (1992). "Quantitative peroxidase-antiperoxidase complex-substrate mass determination in tissue sections by a dual wavelength method". Analytical and Quantitative Cytology and Histology. 14 (2): 73–80. PMID   1590900.
  12. ↑ Cárdenas-Piedra, Lilibeth; Ecker, Rupert C.; Batra, Jyotsna (2024-01-01), "Bioimage Analysis", Reference Module in Life Sciences, Elsevier, doi:10.1016/b978-0-323-95502-7.00147-0, ISBN   978-0-12-809633-8 , retrieved 2025-01-28
  13. ↑ Mungenast, Felicitas; Fernando, Achala; Nica, Robert; Boghiu, Bogdan; Lungu, Bianca; Batra, Jyotsna; Ecker, Rupert C. (April 2021). "Next-Generation Digital Histopathology of the Tumor Microenvironment". Genes. 12 (4): 538. doi: 10.3390/genes12040538 . ISSN   2073-4425. PMC   8068063 . PMID   33917241.
  14. 1 2 3 Mungenast, Felicitas; Fernando, Achala; Nica, Robert; Boghiu, Bogdan; Lungu, Bianca; Batra, Jyotsna; Ecker, Rupert C. (2021-04-07). "Next-Generation Digital Histopathology of the Tumor Microenvironment". Genes. 12 (4): 538. doi: 10.3390/genes12040538 . ISSN   2073-4425. PMC   8068063 . PMID   33917241.
  15. ↑ Gerner, Michael Y.; Kastenmuller, Wolfgang; Ifrim, Ina; Kabat, Juraj; Germain, Ronald N. (2012-08-24). "Histo-cytometry: a method for highly multiplex quantitative tissue imaging analysis applied to dendritic cell subset microanatomy in lymph nodes". Immunity. 37 (2): 364–376. doi:10.1016/j.immuni.2012.07.011. ISSN   1097-4180. PMC   3514885 . PMID   22863836.
  16. ↑ Allard-Chamard, Hugues; Alsufyani, Faisal; Kaneko, Naoki; Xing, Kelly; Perugino, Cory; Mahajan, Vinay S.; Wheat, Joseph L.; Deepe, George S.; Loyd, James; Pillai, Shiv (2021-02-01). "CD4+CTLs in Fibrosing Mediastinitis Linked to Histoplasma capsulatum". The Journal of Immunology. 206 (3): 524–530. doi:10.4049/jimmunol.2000433. ISSN   0022-1767. PMC   7978153 . PMID   33328214.
  17. ↑ Munemura, Ryusuke; Maehara, Takashi; Murakami, Yuka; Koga, Risako; Aoyagi, Ryuichi; Kaneko, Naoki; Doi, Atsushi; Perugino, Cory A.; Della-Torre, Emanuel; Saeki, Takako; Sato, Yasuharu; Yamamoto, Hidetaka; Kiyoshima, Tamotsu; Stone, John H.; Pillai, Shiv (August 2022). "Distinct disease-specific Tfh cell populations in 2 different fibrotic diseases: IgG4-related disease and Kimura disease". The Journal of Allergy and Clinical Immunology. 150 (2): 440–455.e17. doi:10.1016/j.jaci.2022.03.034. ISSN   1097-6825. PMC   10369367 . PMID   35568079.
  18. ↑ Ding, Dah-Ching; Shyu, Woei-Cherng; Lin, Shinn-Zong (2011). "Mesenchymal stem cells". Cell Transplantation. 20 (1): 5–14. doi:10.3727/096368910X. ISSN   1555-3892. PMID   21396235. S2CID   7868466.
  19. ↑ Baer, Patrick C. (2014-07-26). "Adipose-derived mesenchymal stromal/stem cells: An update on their phenotype in vivo and in vitro". World Journal of Stem Cells. 6 (3): 256–265. doi: 10.4252/wjsc.v6.i3.256 . ISSN   1948-0210. PMC   4131268 . PMID   25126376.
  20. ↑ Wong, Tzyy Yue; Chang, Chiung-Hsin; Yu, Chen-Hsiang; Huang, Lynn L. H. (June 2017). "Hyaluronan keeps mesenchymal stem cells quiescent and maintains the differentiation potential over time". Aging Cell. 16 (3): 451–460. doi:10.1111/acel.12567. ISSN   1474-9726. PMC   5418204 . PMID   28474484.
  21. ↑ Pillat, Micheli Mainardi; Oliveira-Giacomelli, Ágatha; das Neves Oliveira, Mona; Andrejew, Roberta; Turrini, Natalia; Baranova, Juliana; Lah Turnšek, Tamara; Ulrich, Henning (February 2021). "Mesenchymal stem cell-glioblastoma interactions mediated via kinin receptors unveiled by cytometry". Cytometry. Part A. 99 (2): 152–163. doi: 10.1002/cyto.a.24299 . ISSN   1552-4930. PMID   33438373. S2CID   231594242.
  22. ↑ Campos, Henrique C.; Ribeiro, Deidiane Elisa; Hashiguchi, Debora; Hukuda, Deborah Y.; Gimenes, Christiane; Romariz, Simone A. A.; Ye, Qing; Tang, Yong; Ulrich, Henning; Longo, Beatriz Monteiro (February 2022). "Distinct Effects of the Hippocampal Transplantation of Neural and Mesenchymal Stem Cells in a Transgenic Model of Alzheimer's Disease". Stem Cell Reviews and Reports. 18 (2): 781–791. doi:10.1007/s12015-021-10321-9. ISSN   2629-3277. PMID   34997526. S2CID   255446340.
  23. ↑ Kaneko, Naoki; Boucau, Julie; Kuo, Hsiao-Hsuan; Perugino, Cory; Mahajan, Vinay S.; Farmer, Jocelyn R.; Liu, Hang; Diefenbach, Thomas J.; Piechocka-Trocha, Alicja; Lefteri, Kristina; Waring, Michael T.; Premo, Katherine R.; Walker, Bruce D.; Li, Jonathan Z.; Gaiha, Gaurav (April 2022). "Temporal changes in T cell subsets and expansion of cytotoxic CD4+ T cells in the lungs in severe COVID-19". Clinical Immunology (Orlando, Fla.). 237: 108991. doi:10.1016/j.clim.2022.108991. ISSN   1521-7035. PMC   8961941 . PMID   35364330.
  24. ↑ Kaneko, Naoki; Kuo, Hsiao-Hsuan; Boucau, Julie; Farmer, Jocelyn R.; Allard-Chamard, Hugues; Mahajan, Vinay S.; Piechocka-Trocha, Alicja; Lefteri, Kristina; Osborn, Matthew; Bals, Julia; Bartsch, Yannic C.; Bonheur, Nathalie; Caradonna, Timothy M.; Chevalier, Josh; Chowdhury, Fatema (2020-10-01). "Loss of Bcl-6-Expressing T Follicular Helper Cells and Germinal Centers in COVID-19". Cell. 183 (1): 143–157.e13. doi:10.1016/j.cell.2020.08.025. ISSN   1097-4172. PMC   7437499 . PMID   32877699.
  25. 1 2 Semeano, Ana T.; Tofoli, Fabiano A.; Corrêa-Velloso, Juliana C.; de Jesus Santos, Ana P.; Oliveira-Giacomelli, Ágatha; Cardoso, Rafaela R.; Pessoa, Mateus A.; da Rocha, Edroaldo Lummertz; Ribeiro, Gustavo; Ferrari, Merari F. R.; Pereira, Lygia V.; Teng, Yang D.; Petri, Denise F. S.; Ulrich, Henning (April 2022). "Effects of Magnetite Nanoparticles and Static Magnetic Field on Neural Differentiation of Pluripotent Stem Cells". Stem Cell Reviews and Reports. 18 (4): 1337–1354. doi:10.1007/s12015-022-10332-0. ISSN   2629-3277. PMID   35325357. S2CID   247678247.
  26. 1 2 Yi, Shanyong; Chen, Ke; Zhang, Lihua; Shi, Weibo; Zhang, Yaxing; Niu, Shiba; Jia, Miaomiao; Cong, Bin; Li, Yingmin (2019). "Endoplasmic Reticulum Stress Is Involved in Stress-Induced Hypothalamic Neuronal Injury in Rats via the PERK-ATF4-CHOP and IRE1-ASK1-JNK Pathways". Frontiers in Cellular Neuroscience. 13: 190. doi: 10.3389/fncel.2019.00190 . ISSN   1662-5102. PMC   6509942 . PMID   31130849.
  27. ↑ Sze, Chun-I.; Lin, Yung-Chieh; Lin, Yuh-Jyh; Hsieh, Ting-Hui; Kuo, Yu Min; Lin, Chyi-Her (2013). "The role of glucocorticoid receptors in dexamethasone-induced apoptosis of neuroprogenitor cells in the hippocampus of rat pups". Mediators of Inflammation. 2013: 628094. doi: 10.1155/2013/628094 . ISSN   1466-1861. PMC   3557631 . PMID   23401645.
  28. ↑ Wang, Liang-Chao; Huang, Chih-Yuan; Wang, Hao-Kuang; Wu, Ming-Hsiu; Tsai, Kuen-Jer (2012-05-01). "Magnesium sulfate and nimesulide have synergistic effects on rescuing brain damage after transient focal ischemia". Journal of Neurotrauma. 29 (7): 1518–1529. doi:10.1089/neu.2011.2030. ISSN   1557-9042. PMC   3335109 . PMID   22332641.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.