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Enregistrement W2036976290 · doi:10.1002/cyto.a.20011

Clinical flow cytometry, a hypothesis‐driven discipline of modern cytomics

2004· review· en· W2036976290 sur OpenAlex
George Janossy

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Notice bibliographique

RevueCytometry Part A · 2004
Typereview
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueSingle-cell and spatial transcriptomics
Établissements canadiensnon disponible
Organismes subventionnairesHealth CanadaSt. Jude Children's Research Hospital
Mots-clésComputer scienceFlow cytometryCytometryMedical laboratoryNanotechnologyMedical physicsMedicinePathologyImmunologyMaterials science

Résumé

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Recently, two major books have been published that summarize the historical aspects and recent achievements of practical flow cytometry (1, 2). Both emphasize the role played by this newly developed technical discipline in the development of scientific (1) and diagnostic platforms during late 20th-century medicine (2). Indeed, the gray box called a flow cytometer is the result of a multidisciplinary collaboration between engineers, biophysicists, biochemists, histopathologists, molecular cytologists, hematologists, immunologists, and quality controllers, with a more recent contribution from physicians specializing in the human immunodeficiency virus (HIV), oncologists, and epidemiologists (Table 1). The foresight by the “fathers” has been astonishing (1). In his book, Howard Shapiro reminds us that flow cytometry, used to investigate cells in a flow system instead of on a static microscope, was put into practice by Coulter when he used impedance to count red cells. Rapid cell spectroscopy was introduced by Kamentsky to leukocyte differential counting with computer-assisted displays. Then optical cell counters, fluorescence dies, lasers, and photomultiplier tubes were added by Fulwyler and Jones. Immunologic concepts and reagents were introduced by the Herzenbergs and were fully primed to harvest the gems of the incipient monoclonal antibody revolution. The first commercial cytometer with precision engineering of the flow cells was launched by Goehde. These initial steps were duly followed by the release of a series of cytometers that showed increasing sensitivity, computerization, and practicality in research and routine laboratory work. This is illustrated by science historians Keating and Cambrosio in their book that blends the sociological aspects of medical technological developments with witness accounts (2). Flow cytometry currently is a colorful, practical discipline that has become available to medicine at the appropriate time for various pressing clinical applications, thus rewarding the scientists who had the vision to foresee these needs. These contributions established cytometry, including flow technology, image analysis, and advanced microscopy, among the leading trends of modern biomedicine and health care. After the launch of the Human Genome Project (3), two areas, genomics and proteomics, have been majestically promoted on both sides of the Atlantic and in the East (4). Genomics includes the identification of genes and gene regulatory processes, and proteomics investigates the abundance of proteins simultaneously with the changes associated with alterations of the functional state of the cell. Such a “pseudo-functional” approach aims to extend the study of quantitative changes during differentiation, proliferation, and signaling of different cell types (5). Clearly, there is an enormous, newly generated influx of information here, but it is not certain that a mere analysis of genes and protein structure, even in its extended format that includes the interaction of various biomolecules, will provide all of the necessary information to understand function and regulation at the level of living cells and organisms. Hence, the concept of cytomics has been introduced recently (6, 7), for two reasons. First, cytomics is the cell-oriented analysis of molecules and their functions in cellular systems and/or organs, referred to as cytomes. Second, cytomics is the new, absolutely essential, interface between biosciences and clinical medicine. Importantly, advanced cytometry, including the traits defined in Table 1, represent the driving engine of cytomics. A new paradigm is that cytomics is the combined, interrelated force for cytometry, proteomics, and genomics (6, 7). There are, however, significant differences between these three modern trends. First, it is in the realm of cytometry, with the major contribution of flow systems, where the appropriate functional analysis of cells, the display and regulation of functional molecules, and the differentiation pathways are successfully carried out. Animal models and human diseases provide targets for these studies. Second, it is the cytometric field where the diagnostic tests, for malignancies (cf. 8,9) and the cellular analysis of infectious diseases (10), currently harvest the richest crop of practical results, whereas in the fields of genomics and proteomics only the seeds are sown for the practically useful multiarray systems. Third, these two fields at their current developmental state, are essentially observational sciences that primarily document the observed heterogeneity. As a marked contrast, the research by flow cytometry has been, from the beginning, primarily hypothesis driven and backed up by sophisticated experimental designs and clearly defined clinical needs. In this review, some of the basic hypotheses that have contributed to the introduction of clinical applications are summarized to document the hypothesis-driven nature of flow cytometry research. Six hypotheses are described that have been conceived with the flow cytometric technology in mind to answer biological and/or clinical diagnostic questions. In each case, a short account of the background is followed by the relevant hypothesis. Then the implications of the results are summarized, with additional observations that substantiate the results observed and explain the significance of the findings. A fluorescence-activated cell sorter (FACS) from Becton-Dickinson, the first to arrive to Europe in 1974, was operated by David Capellaro at the ICRF Tumour Immunology Unit, London (Fig. 1). This sorter was used for leukemia and stem cell research by Greaves and colleagues. Around that time it was already known that the membrane markers for murine and human T and B cells were also present on these cells' malignant counterparts (11, 12). As the common form of acute lymphoid leukemia (ALL) remained negative (non-T, non-B ALL), Greaves and Brown made a rabbit anti–non-T, non-B ALL antiserum (13); in this laboratory other antisera to T cells, myeloid cells, and class II antigens also were used for immunodiagnosis (Fig. 2). The fluorescence activated cell sorter (FACS) at University College London, 1973 to 1974, operated by David Capellaro. The reactivity of heterologous anti-ALL, anti-T, and anti–class II antisera with the common form of ALL (cALL), thymic ALL (Thy-ALL), and a “lymphoid” blast crisis of chronic myeloid leukemia (CML-bc). The results of the absorption of the antiserum made against ALL with Thy-ALL and CML-bc show that the antigen recognized by this diagnostic reagent is shared between them (14). This was the common ALL antigen, with CD10 of 100 k (15, 16). Hypothesis 1A: A non-T, non-B ALL antiserum will react with (a small number of) normal precursor cells, the “target cells,” from which this particular form of malignancy derives (14). The anti-ALL antiserum was shown to react with an antigen of 100 kD (CALLA; later proven to be CD10) (2, 16), strongly expressed on most common ALLs (Fig. 2-1), and weakly expressed on some T-ALL (Fig. 2-7), but undetectable on acute myeloid leukemias and chronic lymphoid leukemia. As predicted, this was the first anti-precursor cell reagent; the reactivity is shown in Table 2. Once this cell type, also referred to as the pro-B cell, and its close relative, the cytoplasmic immunoglobulin (Ig) positive pre-B cells were identified, these cell types were characterized extensively by flow cytometry, cell sorting, biochemistry, and gene rearrangements (2, 14) (also see below). Hypothesis 1B: The same early lymphoid precursor also can be involved in malignancies that may develop as a result of different pathologic mechanism(s), e.g., in Philadelphia chromosome positive (Ph′) chronic myeloid leukemia (CML) of pluripotential hemopoietic stem cell origin (13, 17, 18). Anti-ALL serum reacted with the “lymphoid” blasts in CML (Fig. 2-13) (14, 17). When the anti–non-T, non-B ALL antiserum was cross-absorbed with Ph′ lymphoid blasts (Fig. 2-4) or with T-ALL blasts (Fig. 2-5), the reactivity to common ALL was removed and vice versa (Figs. 2-10 and 2-17). Thus, the antiserum detected the same precursor cell-related moieties (the CD10 antigen) (15, 16) in the different diseases. In Ph′-positive disease that the lymphoid blasts were frequently seen admixed with other cell types such as maturing myeloid cells and myeloblasts (17), cell sorting with FACS showed that only the lymphoid blasts had the CALLA-positive phenotype (Fig. 3) (18, 19). Similar cell sorting studies (20) have shown that class II antigens are expressed more widely on lymphoid and myeloid blasts but not on differentiating forms (Fig. 4). These investigations have established the immunophenotyping platform for leukemias (2). Heterogeneity of cell phenotypes in CML blast crisis: analysis using the FACS. Cells are labeled with anti-cALL serum and separated according to fluorescence as indicated in the lower oscilloscope screen picture. Cells of lymphoid morphology sort into the positive fraction and the nonlymphoid cells in to the negative (18, 19). Heterogeneity of cell types in CML blast crisis. A mixed myeloid and lymphoid blast crisis was stained with an anti–class II antiserum and sorted as in Figure 3. a: The negative fraction contains differentiating granulocytic and erythroid cells. b: In the class II-positive fraction, lymphoid and myeloblasts are seen (20). Hypothesis 2: It is suggested that viable monoclonal hybrids carry surface Ig with antibody specificity and that, after having been labeled with antigen-coated fluorescing microspheres, the progenitors can be sorted into specific antibody secreting fractions to facilitate the selection of “wanted” clones (22). In this study, viable and dead cells could be distinguished by flow analysis. Rare viable hybrids with antigen binding were recognized and enriched by sorting. The antigen-coated fluorospheres adhered to the hybrid cells that expressed antigen-specific receptors, providing a signal bright enough to do the sorting. At the time, only fluorescent microspheres provided a signal bright enough for this purpose. Subsequently, fluorochromes of higher intensity proteins also could be labeled with fluorochromes and used for positively selecting hybridomas of known specificity (23). In culture, the enriched progenitors developed into clones that secreted specific antibodies against the antigens (mouse Ig-1a; Table 3). The beauty of this experiment is that it combined basic science with a practical purpose: to prepare better reagents and to use these in flow cytometry. The successive Leucocyte Typing Workshops marshaled monoclonal antibodies into CD groups (16, 24), and multiparameter analysis became feasible by the tandem use of monoclonal antibodies labeled with different fluorochromes (25). Mike Loken and Leon Terstappen, in collaboration with Curt Civin, had an organized approach to characterizing the expression of these CD groups. The description of erythroid (26) and the B-lymphoid lineage (Fig. 5) (27, 28) was soon followed by the characterization of the neutrophil lineage (29), and monocytic differentiation from CD34+ cells in the bone marrow and cord blood was described by Knapp et al. (30, 31). Stages of B-lymphoid differentiation in the normal bone marrow, investigated with multiparameter analysis using combined staining with monoclonal antibodies labeled with different fluorochromes. The reagents used were CD34, CD10, anti–terminal deoxynucleotidyl transferase, anti–class II (HLA-DR, -DP, and -DQ), CD19, CD20, CD21, and IgM (for membrane labeling; SmIgM). B-lymphoid precursors at phase II contain cytoplasmic IgM (not shown); see Greaves and Janossy (14). From Loken et al. (27, 28). Hypothesis 3: Flow cytometry, with its multiparameter analytical approach, is ideally suited to indicate the steps of differentiation along a given cell lineage in situ (e.g., in the bone marrow) (27, 28) and to prove this scheme by following the development of lineage precursors in vitro (30-32). The example of B-lymphoid development from early precursors, through CALLA (CD10)–positive pro-B cells to Sigma+ B lymphocytes (Fig. 5) is a model for the highly controlled sequential acquisition of cell surface antigens during B-lymphocyte development. The most immature cells identifiable in the bone marrow express CD34 and HLA-DR. The earliest recognizable B-lineage cells (CD19+, bright CD10+) also express CD34 and terminal deoxynucleotidyl transferase in the nucleus and are proliferating. The progression of cells from stage II to stage III is marked by the acquisition of CD20, HLA-DQ, and SmIgM. A caveat is that SmIgD+ B cells (last stage) may not be an integral or sequential part of this linear progression but perhaps represent cells that seed back to bone marrow from the periphery (33). The cultivation of CD34+ precursor cells in vitro has confirmed these B-cell and myeloid differentiation schemes (30, 31). Hypothesis 4: Differentiating normal cells express their markers in an orderly fashion when studied with three-color (35) or four-color (36, 37) antibody combinations, whereas leukemia cells frequently show aberrant dysregulation that is their characteristic feature. In patients retested at the end of remission induction in full morphologic remission with sensitive techniques to identify MRD, the presence of aberrant cells may have a profound prognostic significance (38). The regularity of marker expression in normal bone marrow for CD10/CD20/CD19 (Fig. 6a) and CD34/CD38/CD19 combinations (Fig. 6b) is depicted. The three populations of normal precursors cell types such as the early (CD34+, CD10+) precursors, intermediate forms and the newly emerging (CD10−, CD20+) B cells are shown as dots (with colours in ref. 35). In the white unallocated areas, aberrant leukemic phenotypes reside, representing 59.7% and 53.9% of ALL cases; the frequency of ALL cases that exhibit the individual aberrant combinations is also shown (35). Identification of leukemic blast cells of the B-lymphoid lineage by irregular aberrant marker expression. Normal B-cell precursors provide a regular differentiation pathway that can be investigated by different combinations such as CD10/CD20/CD19 (a) or CD34/CD38/CD19 (b). Leukemic cells frequently fall outside these normal boundaries and occupy different positions in the “white area.” In a and b, 53% to 59% of ALL cases can be regarded as aberrant, but other combinations exist, and most cases of ALL can be followed with one or another combination staining. The percentage values are the proportions of ALL cases that are positioned as aberrant. For example, 10.4% of cases show much higher CD10 display on ALL blasts than on normal CD20+ B-cell forms (35). These for are regarded to be useful can identify one leukemic cells normal cells. The and four-color combinations used for and B-lineage ALL and for acute myeloid leukemia at the and at the University are published in In clinical the are studied at the end of remission induction by morphologic is the bone marrow is retested by flow cytometry for this is more sensitive to leukemia. The prognostic significance of these diagnostic results has been proven in a by the that developed during The patients were into (a) MRD, but than between and and than the were and the clinical use of the (36, These for are to molecular Both of these as in the clinical of the new The studies described already the that the marker expression on normal was regular and that the flow cytometers provided quantitative These can be studied by that expressed as molecules cell the tests, such as the is the most the first antibody used to to the membrane antigens at and the used at is a reagent used in Thus, the results observed are and the of for the expression of different membrane antigens on different cell types For example, the antigens on lymphocytes are expressed at higher than antigens on T cells and higher than antigens on B lymphocytes of CD19, and antigens on lymphocytes from different (a) and the of these antigens on cell from cord blood cells (b). The was by using the quantitative referred to as the The of and molecules is regular and these moieties can be used as for quantitative flow cytometry Once the values are these to the staining of normal lymphocytes and aberrant populations in a quantitative Hypothesis When by the some markers will prove to be at their characteristic known of expression in different to be used as biological these molecules be by to provide cellular of The values for the expression of various CD antigens on and have been using the The of expression is known and the individual for and antigen expression are than (Fig. These also on cord blood cells for and are at a level on blood for use (Fig. These of as in the cellular the of these during flow cytometry, the values of fluorescence intensity can be by values of In normal T cells express (Fig. cells are T cells exhibit molecules, whereas the cells display to 100 (Fig. This is by the of populations that can be or in cases can aberrant T cells be seen in higher than In such a (Fig. the cells are T cells with display chronic T chronic lymphoid These quantitative on normal blood and on blood have a in characterizing the aberrant display of various antigens in leukemia and The of is the in the of The level of these cells in the blood is the most for disease associated with the first on immunodeficiency flow cytometry has remained the for in Recently, however, this has been into The introduction of in has the for but the of has been to as as to For these have been to the relevant to be First, physicians in may a full of analysis and whereas in with a only Second, the of recently has been established when with the on morphologic a of antibody reactivity be used when lymphocytes are defined by (Fig. The for this to a more is that markers such as and are in at the time when It is to count T lymphocytes using a antibody labeled with a of fluorochromes such as or using intensity on the and on the is the small lymphocytes are from the The using a antibody provide when with other that use more reagents such as the on after the are not as as flow cytometry it is as by the values with the flow cytometric The cases show This from activated and/or in patients with infectious disease such as Hypothesis flow cytometry for can be and by using a with the current these will be more the more Hypothesis The of counting by flow is that this quantitative between lymphocytes and are to this additional for and to use when is associated with such as and diseases. staining on the flow cytometer between T cells of of of the fluorochromes used (Fig. The such as however, antigen in blood after cells have been normal when most antigens from the and antigens are into the background during the of the in patients with and this and with T cells be referred to as T are that do not to when by flow cytometry (Fig. This is on in diseases to at the but these have been by molecules It is to the of from the currently when these are on the of a and counting the for patients during the of new information on genes and the of genomics and proteomics are to be in an to the functional of cells, cellular systems, and It is an development that the discipline of with its technical driving force by flow cytometry, image analysis, and modern microscopy, has been recently to to a new phase of scientific development (6, 7). is an approach cells and systems using the available information in a functional The of this approach is by the that the using flow cytometry as the technology, have been, from their early of development to the present hypothesis (Fig. The concept of cytomics includes the three of proteomics, and cytometry to cellular and functions and to biosciences with their medical is a hypothesis-driven as illustrated in this In this review, the have been primarily to document two aspects of the hypotheses (a) their to the science to diagnostic medicine to provide for the that cytomics a new interface between the biosciences and clinical and to provide practical in to scientific In the field by flow cytometry is than suggested The of science that is by the of Leucocyte Typing Workshops by cellular through the monoclonal antibody technology and their CD the achievements of the proteomics discipline and is more to to its essentially functional The of the monoclonal antibody on flow cytometry vice is It is to see these two of science CD and can in this review, the modern of flow cytometry to develop of analysis by lasers, optical and of antibodies with new of fluorochromes have not been these investigations are to sort the of in such as and in and human such as the of the newly regulatory T cells the emerging new platforms show the of the diagnostic systems with flow cytometric analysis of cell including This is the concept which is more than its This development is a example to two (a) the current highly multiarray systems, with contributions from will soon clinical in a and flow cytometry is an part of the current technical in the of multiarray to Greaves London, of the of for Howard Shapiro David and for also to of for to his (Fig.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,988
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,002
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,093
Tête enseignante GPT0,360
Écart entre enseignants0,267 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle