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Cell size and cell count are adaptively regulated and intimately linked to growth and function. Yet, despite their widespread relevance, the relation between cell size and count has never been formally examined over the whole human body. Here, we compile a comprehensive dataset of cell size and count over all major cell types, with data drawn from >1,500 published sources. We consider the body of a representative male (70 kg), which allows further estimates of a female (60 kg) and 10-y-old child (32 kg). We build a hierarchical interface for the cellular organization of the body, giving easy access to data, methods, and sources (https://humancelltreemap.mis.mpg.de/). In total, we estimate total body counts of ≈36 trillion cells in the male, ≈28 trillion in the female, and ≈17 trillion in the child. These data reveal a surprising inverse relation between cell size and count, implying a trade-off between these variables, such that all cells within a given logarithmic size class contribute an equal fraction to the body's total cellular biomass. We also find that the coefficient of variation is approximately independent of mean cell size, implying the existence of cell-size regulation across cell types. Our data serve to establish a holistic quantitative framework for the cells of the human body, and highlight large-scale patterns in cell biology.
Conventional circulating tumor cell (CTC) enumeration could ignore the CTCs more relevant to cancer metastasis. Thus, negative selection CTC enumeration was proposed, by which information on two cellular biomarkers (numbers of CTCs and CD45neg EpCAMneg cells) can be obtained. By combining this approach with the conventional biomarker carcinoembryonic antigen (CEA), this study aimed to explore whether any combination of these biomarkers could improve the predictive performance for colorectal cancer (CRC) or its status. In this work, these two cell populations in healthy donors and CRC patients were quantified. Results revealed that enumeration of these two cell populations was able to discriminate healthy donors from CRC patients, even patients with non-advanced CRC. Moreover, the combination of the two cell populations showed improved performance (AUROC: 0.893) for CRC prediction over the use of only one population. Compared with CEA alone, the combination of the three biomarkers increased the performance (AUROC) for advanced CRC prediction from 0.643 to 0.727. Compared with that of CEA alone for metastatic CRC prediction, the AUROC was increased from 0.780 to 0.837 when the CTC count was included. Overall, this study demonstrated that the combination of these two cellular biomarkers with CEA improved the predictive performance for CRC and its status.
Biofilms are the preferred sessile and matrix-embedded life form of most microorganisms on surfaces. In the medical field, biofilms are a frequent cause of treatment failure because they protect the bacteria from antibiotics and immune cells. Antibiotics are selected according to the minimal inhibitory concentration (MIC) based on the planktonic form of bacteria. Determination of the minimal biofilm eradicating concentration (MBEC), which can be up to 1,000-fold greater than the MIC, is not currently conducted as routine diagnostic testing, primarily because of the methodical hurdles of available biofilm assessing protocols that are time- and cost-consuming. Comparative analysis of biofilms is also limited as most quantitative methods such as crystal violet staining are indirect and highly imprecise. In this paper, we present a novel algorithm for assessing biofilm resistance to antibiotics that overcomes several of the limitations of alternative methods. This algorithm aims for a computer-based analysis of confocal microscope 3D images of biofilms after live/dead stains providing various biofilm parameters such as numbers of viable and dead cells and their vertical distributions within the biofilm, or biofilm thickness. The performance of this algorithm was evaluated using computer-simulated 2D and 3D images of coccal and rodent cells varying different parameters such as cell density, shading or cell size. Finally, genuine biofilms that were untreated or treated with nitroxoline or colistin were analyzed and the results were compared with quantitative microbiological standard methods. This novel algorithm allows a direct, fast and reproducible analysis of biofilms after live/dead staining. It performed well in biofilms of moderate cell densities in a 2D set-up however the 3D analysis remains still imperfect and difficult to evaluate. Nevertheless, this is a first try to develop an easy but conclusive tool that eventually might be implemented into routine diagnostics to determine the MBEC and to improve outcomes of patients with biofilm-associated infections.
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
In esophageal squamous cell carcinoma, an elevated preoperative absolute monocyte count (Pre-AMC) is reported to be a predictor of survival, but the clinical application of postoperative absolute monocyte count change (AMCc) remains unknown. The present study was designed to investigate the prognostic value of AMCc in ESCC. 686 patients of ESCC after radical surgery without preoperative adjuvant therapy were enrolled. The Pre-AMC and AMCc were recorded within one week before the operation and one week after surgery. We considered the median of Pre-AMC as the optimal cut-off value to evaluate the relationship between Pre-AMC and patient survival. AMCc was defined as AMCc increased (higher than Pre-AMC) and AMCc decreased (lower than Pre-AMC). Demographic and clinical characteristics, disease-free survival (DFS), and overall survival (OS) were statistically analyzed. Multivariate analysis revealed that AMCc was a better independent prognostic factor for both OS (P = 0.002, HR = 0.614, 95% CI 0.450-0.837) and DFS (P = 0.023, HR = 0.656, 95% CI 0.456-0.943) than Pre-AMC which was only an independent prognostic factor for OS (P = 0.033, HR = 2.031, 95% CI 1.058-3.898). AMCc could be a better prognostic factor than Pre-AMC in patients with ESCC. AMCc decrease predicts worse OS and DFS in ESCC undergoing curative resection.
Monoclonal B-cell lymphocytosis (MBL) is a preclinical hematologic syndrome characterized by small accumulations of CD5(+) B lymphocytes. Most MBL share phenotypic characteristics with chronic lymphocytic leukemia (CLL). Although some MBL progress to CLL, most MBL have apparently limited potential for progression to CLL, particularly those MBL with normal absolute B-cell counts ('low-count' MBL). Most CLL are monoclonal and it is not known whether MBL are monoclonal or oligoclonal; this is important because it is unclear whether MBL represent indolent CLL or represent a distinct premalignant precursor before the development of CLL. We used flow cytometry analysis and sorting to determine immunophenotypic characteristics, clonality and molecular features of MBL from familial CLL kindreds. Single-cell analysis indicated four of six low-count MBL consisted of two or more unrelated clones; the other two MBL were monoclonal. 87% of low-count MBL clones had mutated immunoglobulin genes, and no immunoglobulin heavy-chain rearrangements of V(H) family 1 were observed. Some MBL were diversified, clonally related populations with evidence of antigen drive. We conclude that although low-count MBL share many phenotypic characteristics with CLL, many MBL are oligoclonal. This supports a model for step-wise development of MBL into CLL.
The aim of this study was to evaluate complete blood cell count parameters including red blood cell indices, white blood cell subtypes, and platelet indices for predicting deep vein thrombosis (DVT). A total of 71 (44 male and 27 female) patients with acute femoral and popliteal DVT diagnosed by doppler ultrasonography during a period of seven years (2011-2017) were included in the study. By matching age and gender, 142 (88 male and 54 female) subjects diagnosed with venous insufficiency in the same time interval were assigned as control group. Data were obtained by reviewing hospital records of the study participants, including clinical and demographic characteristics and complete blood cell parameters. Frequencies of hypertension, diabetes mellitus, chronic obstructive pulmonary disease, chronic renal failure, and coronary arterial disease were higher in DVT group as compared to non-DVT group (p<0.05). Hemoglobin and lymphocyte values were lower, and red blood cell distribution width, neutrophil, neutrophil to lymphocyte ratio, and platelet to lymphocyte ratio higher in DVT group as compared with non-DVT group (p<0.05). There was no significant between-group difference in terms of mean corpuscular volume, platelet, mean platelet volume, mean platelet volume to platelet ratio, and platelet distribution width (p>0.05). Hypertension, hemoglobin, neutrophil to lymphocyte ratio, and platelet to lymphocyte ratio were independent risk factors for DVT. We found that hypertension, anemia, neutrophil to lymphocyte ratio, and platelet to lymphocyte ratio were independent risk factors for DVT. In particular, neutrophil to lymphocyte ratio and hemoglobin may be used as novel, inexpensive, and reliable diagnostic tools for DVT.
Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at a cellular resolution. However, noise due to amplification and dropout may obstruct analyses, so scalable denoising methods for increasingly large but sparse scRNA-seq data are needed. We propose a deep count autoencoder network (DCA) to denoise scRNA-seq datasets. DCA takes the count distribution, overdispersion and sparsity of the data into account using a negative binomial noise model with or without zero-inflation, and nonlinear gene-gene dependencies are captured. Our method scales linearly with the number of cells and can, therefore, be applied to datasets of millions of cells. We demonstrate that DCA denoising improves a diverse set of typical scRNA-seq data analyses using simulated and real datasets. DCA outperforms existing methods for data imputation in quality and speed, enhancing biological discovery.
Some autoimmune (AI) conditions affect white blood cell (WBC) counts. Whether a genetic predisposition to AI disease associates with WBC counts in populations expected to have low numbers of AI cases is not known. We developed genetic instruments for 7 AI diseases using genome-wide association study summary statistics. Two-sample inverse variance weighted regression (IVWR) was used to determine associations between each instrument and WBC counts. Effect size represents change in transformed WBC counts per change in log odds-ratio of the disease. For AI diseases with significant associations by IVWR, polygenic risk scores (PRS) were used to test for associations with measured WBC counts in individuals of European ancestry in a community-based (ARIC, n = 8926), and a medical-center derived cohort (BioVU, n = 40,461). The IVWR analyses revealed significant associations between 3 AI diseases and WBC counts: systemic lupus erythematous (Beta = - 0.05 [95% CI, - 0.06, - 0.03]), multiple sclerosis (Beta = - 0.06 [- 0.10, - 0.03]), and rheumatoid arthritis (Beta = 0.02 [0.01, 0.03]). PRS for these diseases showed associations with measured WBC counts in ARIC and BioVU. Effect sizes tended to be larger among females, consistent with the known higher prevalence of these diseases among this group. This study shows that genetic predisposition to systemic lupus erythematosus, rheumatoid arthritis, and multiple sclerosis was associated with WBC counts, even in populations expected to have very low numbers of disease cases.
Cell proliferation is critical to the outgrowth of biological structures including the face and limbs. This cellular process has traditionally been studied via sequential histological sampling of these tissues. The length and tedium of traditional sampling is a major impediment to analyzing the large datasets required to accurately model cellular processes. Computerized cell localization and quantification is critical for high-throughput morphometric analysis of developing embryonic tissues. We have developed the Incremental Cell Search (ICS), a novel software tool that expedites the analysis of relationships between morphological outgrowth and cell proliferation in embryonic tissues. Based on an estimated average cell size and stain color, ICS rapidly indicates the approximate location and amount of cells in histological images of labeled embryonic tissue and provides estimates of cell counts in regions with saturated fluorescence and blurred cell boundaries. This capacity opens the door to high-throughput 3D and 4D quantitative analyses of developmental patterns.
Several studies have investigated the influence of clinical and biological variables on mobilisation of peripheral blood progenitor cells (PBPCs). The aim of this study was to evaluate the role of steady-state bone marrow (BM) CD34+ cells as a predictive parameter of PBPC yield. We studied 90 patients with multiple myeloma (MM) (41 patients), non-Hodgkin's lymphoma (NHL) (25 patients) or acute myeloid leukaemia (AML) (24 patients), mobilised with chemotherapy and growth factor. The median time from first treatment to mobilisation was 5 months. Only one patient was previously exposed to alkylating agents. The median BM CD34+ count at mobilisation was 833 microl(-1) (range: 1.4-15.540) corresponding to 1.51% of mononuclear cells (range: 0.02-8.6). Sixty-six patients (73%) reached the optimal target of 4 x 10(6) kg(-1) CD34+ cells with 1 (18 patients), 2 (42 patients) or 3 leukaphereses (6 patients). Eleven patients (12%) mobilised less than 4 x 10(6) kg(-1) CD34+ cells and 13 (15%) failed mobilisation. Among the laboratory and clinical parameters evaluated at the time of mobilisation, only BM CD34+ count was a predictive factor for adequate collection (P = 0.04), particularly in MM patients (P = 0.003). In this setting, a BM concentration of CD34+ cells lower than 66 microL(-1) was associated with a higher probability of inadequate collection.
CD4+ T-cell count External Quality Assessment program is important for the evaluation of performance of CD4 count laboratories. The aim of this study was to assess the quality of CD4count laboratory performance using in-house Proficiency testing panels that perform routine CD4 counts in Addis Ababa, Ethiopia, 2013/14.
There is a continuous debate on how to adequately evaluate long-term CD4+ cell count in response to combination antiretroviral therapy (ART) among human immunodeficiency virus (HIV)-infected individuals. Our study evaluated the long-term CD4+ cell count response (up to ten years) after initiation of ART and described the differences in the CD4+ cell count response stratified by pretreatment CD4+ cell count, and other socio-demographic, behavioral, and clinical factors.
During vertebrate development, many types of precursor cell divide a limited number of times before they stop and terminally differentiate. It is unclear what limits cell proliferation and causes the cells to stop dividing when they do. The stopping mechanisms are important as they influence both the number of differentiated cells generated and the timing of differentiation. We have been studying the 'stopping' problem in the oligodendrocyte cell lineage [1] [2], which is responsible for myelination in the vertebrate central nervous system. Previous studies demonstrated that the proliferation of oligodendrocyte precursor cells isolated from the developing rat optic nerve is limited by an intrinsic 'clock' mechanism [3], which consists of two components: a counting mechanism that counts time or cell divisions, and an effector mechanism that arrests the cell cycle and initiates cell differentiation when the appropriate time is reached [4] [5]. In the present study, we address the question of whether the counting mechanism operates by counting cell divisions. We show that precursor cells cultured at 33 degrees C divide more slowly but stop dividing and differentiate sooner, after fewer cell divisions, than when they are cultured at 37 degrees C, indicating that the counting mechanism does not count cell divisions but measures time in some other way. In addition, we show that the levels of the cyclin-dependent kinase inhibitor p27(Kip1) (p27) rise faster at 33 degrees C than at 37 degrees C, consistent with previous evidence [6] that the accumulation of p27 may be part of the counting mechanism.
Mammalian cells transform into individual tubular straw cells naturally in tissues and in response to desiccation related stress in vitro. The transformation event is characterized by a dramatic cellular deformation process which includes: condensation of certain cellular materials into a much smaller tubular structure, synthesis of a tubular wall and growth of filamentous extensions. This study continues the characterization of straw cells in blood, as well as the mechanisms of tubular transformation in response to stress; with specific emphasis placed on investigating whether tubular transformation shares the same signaling pathway as apoptosis.
Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals. Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data. Using different simulated gene panels that overrepresent genes expressed in specific tissue anatomical regions or cell types, we find that normalization methods that use scaling factors derived from gene counts differentially impact normalized gene expression magnitudes in a region- or cell type-specific manner. We show that these normalization-induced effects may reduce the reliability of downstream differential gene expression and fold change analysis, introducing false positive and false negative results when compared to results obtained from gene panels that are more representative of the gene expression of the tissue's component cell types. These effects are not observed without normalization or when scaling factors are not derived from gene counts, such as with cell volume normalization. Overall, we caution that the choice of normalization method and gene panel may impact the biological interpretation of the im-SRT data.
The identification of novel predictors of poor outcome may help stratify cardiovascular risk. Aim was to evaluate the individual contribution of blood cell count parameters, as well as their clustering, on the risk of death and cardiovascular events over the long term in the population-based Malmö Diet and Cancer Study cohort.
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