The transcriptional regulation of normal and malignant blood cell development.

Development of multicellular organisms requires the differential usage of our genetic information to change one cell fate into another. This process drives the appearance of different cell types that come together to form specialized tissues sustaining a healthy organism. In the last decade, by moving away from studying single genes toward a global view of gene expression control, a revolution has taken place in our understanding of how genes work together and how cells communicate to translate the information encoded in the genome into a body plan. The development of hematopoietic cells has long served as a paradigm of development in general. In this review, we highlight how transcription factors and chromatin components work together to shape the gene regulatory networks controlling gene expression in the hematopoietic system and to drive blood cell differentiation. In addition, we outline how this process goes astray in blood cancers. We also touch upon emerging concepts that place these processes firmly into their associated subnuclear structures adding another layer of the control of differential gene expression.

Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies.

Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static phenotypic readouts. Transmitted light images, on the other hand, provide direct morphological readouts and can be acquired over time to provide a rich data source for dynamic cell phenotypic state identification. Here, we describe an end-to-end deep learning platform, UPSIDE (Unsupervised Phenotypic State IDEntification), for discovering cell states and their dynamics from transmitted light movies. UPSIDE uses the variational auto-encoder architecture to learn latent cell representations, which are then clustered for state identification, decoded for feature interpretation, and linked across movie frames for transition rate inference. Using UPSIDE, we identified distinct blood cell types in a heterogeneous dataset. We then analyzed movies of patient-derived acute myeloid leukemia cells, from which we identified stem-cell associated morphological states as well as the transition rates to and from these states. UPSIDE opens up the use of transmitted light movies for systematic exploration of cell state heterogeneity and dynamics in biology and medicine.

Robust Blood Cell Image Segmentation Method Based on Neural Ordinary Differential Equations.

For the analysis of medical images, one of the most basic methods is to diagnose diseases by examining blood smears through a microscope to check the morphology, number, and ratio of red blood cells and white blood cells. Therefore, accurate segmentation of blood cell images is essential for cell counting and identification. The aim of this paper is to perform blood smear image segmentation by combining neural ordinary differential equations (NODEs) with U-Net networks to improve the accuracy of image segmentation. In order to study the effect of ODE-solve on the speed and accuracy of the network, the ODE-block module was added to the nine convolutional layers in the U-Net network. Firstly, blood cell images are preprocessed to enhance the contrast between the regions to be segmented; secondly, the same dataset was used for the training set and testing set to test segmentation results. According to the experimental results, we select the location where the ordinary differential equation block (ODE-block) module is added, select the appropriate error tolerance, and balance the calculation time and the segmentation accuracy, in order to exert the best performance; finally, the error tolerance of the ODE-block is adjusted to increase the network depth, and the training NODEs-UNet network model is used for cell image segmentation. Using our proposed network model to segment blood cell images in the testing set, it can achieve 95.3% pixel accuracy and 90.61% mean intersection over union. By comparing the U-Net and ResNet networks, the pixel accuracy of our network model is increased by 0.88% and 0.46%, respectively, and the mean intersection over union is increased by 2.18% and 1.13%, respectively. Our proposed network model improves the accuracy of blood cell image segmentation and reduces the computational cost of the network.

Label-free hematology analysis using deep-ultraviolet microscopy.

Hematological analysis, via a complete blood count (CBC) and microscopy, is critical for screening, diagnosing, and monitoring blood conditions and diseases but requires complex equipment, multiple chemical reagents, laborious system calibration and procedures, and highly trained personnel for operation. Here we introduce a hematological assay based on label-free molecular imaging with deep-ultraviolet microscopy that can provide fast quantitative information of key hematological parameters to facilitate and improve hematological analysis. We demonstrate that this label-free approach yields 1) a quantitative five-part white blood cell differential, 2) quantitative red blood cell and hemoglobin characterization, 3) clear identification of platelets, and 4) detailed subcellular morphology. Analysis of tens of thousands of live cells is achieved in minutes without any sample preparation. Finally, we introduce a pseudocolorization scheme that accurately recapitulates the appearance of cells under conventional staining protocols for microscopic analysis of blood smears and bone marrow aspirates. Diagnostic efficacy is evaluated by a panel of hematologists performing a blind analysis of blood smears from healthy donors and thrombocytopenic and sickle cell disease patients. This work has significant implications toward simplifying and improving CBC and blood smear analysis, which is currently performed manually via bright-field microscopy, and toward the development of a low-cost, easy-to-use, and fast hematological analyzer as a point-of-care device and for low-resource settings.

Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images.

Previous works on segmentation of SEM (scanning electron microscope) blood cell image ignore the semantic segmentation approach of whole-slide blood cell segmentation. In the proposed work, we address the problem of whole-slide blood cell segmentation using the semantic segmentation approach. We design a novel convolutional encoder-decoder framework along with VGG-16 as the pixel-level feature extraction model. The proposed framework comprises 3 main steps: First, all the original images along with manually generated ground truth masks of each blood cell type are passed through the preprocessing stage. In the preprocessing stage, pixel-level labeling, RGB to grayscale conversion of masked image and pixel fusing, and unity mask generation are performed. After that, VGG16 is loaded into the system, which acts as a pretrained pixel-level feature extraction model. In the third step, the training process is initiated on the proposed model. We have evaluated our network performance on three evaluation metrics. We obtained outstanding results with respect to classwise, as well as global and mean accuracies. Our system achieved classwise accuracies of 97.45%, 93.34%, and 85.11% for RBCs, WBCs, and platelets, respectively, while global and mean accuracies remain 97.18% and 91.96%, respectively.

Expert knowledge for the recognition of leukemic cells.

This work shows the advantage of expert knowledge for leukemic cell recognition. In the medical area, visual analysis of microscopic images has regularly used biological samples to recognize hematological disorders. Nowadays, techniques of image recognition are needed to achieve an adequate identification of blood tissues. This paper presents a procedure to acquire expert knowledge from blood cell images. We apply Gaussian mixtures, evolutionary computing, and standard techniques of image processing to extract knowledge. This information feeds a support vector machine or multilayer perceptron to classify healthy or leukemic cells. Additionally, convolutional neural networks are used as a benchmark to compare our proposed method with the state of the art. We use a public database of 260 healthy and leukemic cell images. Results show that our traditional pattern recognition methodology matches deep learning accuracy since the recognition of blood cells achieves 99.63%, whereas the convolutional neural networks reach 97.74% on average. Moreover, the computational effort of our approach is minimal, while meeting the requirement of being explainable.

Blood Cell Classification Based on Hyperspectral Imaging With Modulated Gabor and CNN.

Cell classification, especially that of white blood cells, plays a very important role in the field of diagnosis and control of major diseases. Compared to traditional optical microscopic imaging, hyperspectral imagery, combined with both spatial and spectral information, provides more wealthy information for recognizing cells. In this paper, a novel blood cell classification framework, which combines a modulated Gabor wavelet and deep convolutional neural network (CNN) kernels, named as MGCNN, is proposed based on medical hyperspectral imaging. For each convolutional layer, multi-scale and orientation Gabor operators are taken dot product with initial CNN kernels. The essence is to transform the convolutional kernels into the frequency domain to learn features. By combining characteristics of Gabor wavelets, the features learned by modulated kernels at different frequencies and orientations are more representative and discriminative. Experimental results demonstrate that the proposed model can achieve better classification performance than traditional CNNs and widely used support vector machine approaches, especially as training small-sample-size situations.

Targeting individual cells by barcode in pooled sequence libraries.

Transcriptional profiling of thousands of single cells in parallel by RNA-seq is now routine. However, due to reliance on pooled library preparation, targeting analysis to particular cells of interest is difficult. Here, we present a multiplexed PCR method for targeted sequencing of select cells from pooled single-cell sequence libraries. We demonstrated this molecular enrichment method on multiple cell types within pooled single-cell RNA-seq libraries produced from primary human blood cells. We show how molecular enrichment can be combined with FACS to efficiently target ultra-rare cell types, such as the recently identified AXL+SIGLEC6+ dendritic cell (AS DC) subset, in order to reduce the required sequencing effort to profile single cells by 100-fold. Our results demonstrate that DNA barcodes identifying cells within pooled sequencing libraries can be used as targets to enrich for specific molecules of interest, for example reads from a set of target cells.

Unbiased classification of mosquito blood cells by single-cell genomics and high-content imaging.

Mosquito blood cells are immune cells that help control infection by vector-borne pathogens. Despite their importance, little is known about mosquito blood cell biology beyond morphological and functional criteria used for their classification. Here, we combined the power of single-cell RNA sequencing, high-content imaging flow cytometry, and single-molecule RNA hybridization to analyze a subset of blood cells of the malaria mosquito Anopheles gambiae By demonstrating that blood cells express nearly half of the mosquito transcriptome, our dataset represents an unprecedented view into their transcriptional program. Analyses of differentially expressed genes identified transcriptional signatures of two cell types and provide insights into the current classification of these cells. We further demonstrate the active transfer of a cellular marker between blood cells that may confound their identification. We propose that cell-to-cell exchange may contribute to cellular diversity and functional plasticity seen across biological systems.

Quantitative distinction of the morphological characteristic of erythrocyte precursor cells with texture analysis using gray level co-occurrence matrix.

BACKGROUND: Morphological characteristics of blood cells are still qualitatively defined. So a texture analysis (Tx) method using gray level co-occurrence matrices (GLCMs; CM-Tx method) was applied to images of erythrocyte precursor cells (EPCs) for quantitatively distinguishing four types of EPC stages: proerythroblast, basophilic erythroblast, polychromatic erythroblast, and orthochromatic erythroblast. METHODS: Fifty-five images of four types of EPCs were downloaded from an atlas uploaded by the Blood Cell Morphology Standardization Subcommittee (BCMSS) of the Japanese Society of Laboratory Hematology (JSLH). Using in-house programs, two types of GLCMs-(R: d=1, θ=0°) and (U: d=1, θ=270°)-and nine types of texture distinction index (TDI) were calculated with images removed outer part of cell. RESULTS: Three binary decision trees were sequentially divided among four types of EPC with the sum average of GLCM (U), the contrast of GLCM (R), and the sum average of GLCM (U). The average concordance rate (sensitivity) of CM-Tx method with the judgments of eleven experts in the BCMSS of the JSLH was 95.8% (87.5-100.0), and the average specificity was 97.6% (92.5-100.0). CONCLUSIONS: The CM-Tx method is an effective tool for quantitative distinction of EPC with their morphological features.

Gating mass cytometry data by deep learning.

MOTIVATION: Mass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods for analyzing CyTOF data attempt to improve automation, scalability, performance and interpretation of data generated in large studies. Assigning individual cells into discrete groups of cell types (gating) involves time-consuming sequential manual steps, untenable for larger studies. RESULTS: We introduce DeepCyTOF, a standardization approach for gating, based on deep learning techniques. DeepCyTOF requires labeled cells from only a single sample. It is based on domain adaptation principles and is a generalization of previous work that allows us to calibrate between a target distribution and a source distribution in an unsupervised manner. We show that DeepCyTOF is highly concordant (98%) with cell classification obtained by individual manual gating of each sample when applied to a collection of 16 biological replicates of primary immune blood cells, even when measured across several instruments. Further, DeepCyTOF achieves very high accuracy on the semi-automated gating challenge of the FlowCAP-I competition as well as two CyTOF datasets generated from primary immune blood cells: (i) 14 subjects with a history of infection with West Nile virus (WNV), (ii) 34 healthy subjects of different ages. We conclude that deep learning in general, and DeepCyTOF specifically, offers a powerful computational approach for semi-automated gating of CyTOF and flow cytometry data. AVAILABILITY AND IMPLEMENTATION: Our codes and data are publicly available at https://github.com/KlugerLab/deepcytof.git. CONTACT: yuval.kluger@yale.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CD1- and CD1+ porcine blood dendritic cells are enriched for the orthologues of the two major mammalian conventional subsets.

Conventional dendritic cells (cDC) are professional antigen-presenting cells that induce immune activation or tolerance. Two functionally specialised populations, termed cDC1 and cDC2, have been described in humans, mice, ruminants and recently in pigs. Pigs are an important biomedical model species and a key source of animal protein; therefore further understanding of their immune system will help underpin the development of disease prevention strategies. To characterise cDC populations in porcine blood, DC were enriched from PBMC by CD14 depletion and CD172a enrichment then stained with lineage mAbs (Lin; CD3, CD8α, CD14 and CD21) and mAbs specific for CD172a, CD1 and CD4. Two distinct porcine cDC subpopulations were FACSorted CD1- cDC (Lin-CD172+ CD1-CD4-) and CD1+ cDC (Lin-CD172a+ CD1+ CD4-), and characterised by phenotypic and functional analyses. CD1+ cDC were distinct from CD1- cDC, expressing higher levels of CD172a, MHC class II and CD11b. Following TLR stimulation, CD1+ cDC produced IL-8 and IL-10 while CD1- cDC secreted IFN-α, IL-12 and TNF-α. CD1- cDC were superior in stimulating allogeneic T cell responses and in cross-presenting viral antigens to CD8 T cells. Comparison of transcriptional profiles further suggested that the CD1- and CD1+ populations were enriched for the orthologues of cDC1 and cDC2 subsets respectively.

Cell type specific DNA methylation in cord blood: A 450K-reference data set and cell count-based validation of estimated cell type composition.

Epigenome-wide association studies of prenatal exposure to different environmental factors are becoming increasingly common. These studies are usually performed in umbilical cord blood. Since blood comprises multiple cell types with specific DNA methylation patterns, confounding caused by cellular heterogeneity is a major concern. This can be adjusted for using reference data consisting of DNA methylation signatures in cell types isolated from blood. However, the most commonly used reference data set is based on blood samples from adult males and is not representative of the cell type composition in neonatal cord blood. The aim of this study was to generate a reference data set from cord blood to enable correct adjustment of the cell type composition in samples collected at birth. The purity of the isolated cell types was very high for all samples (>97.1%), and clustering analyses showed distinct grouping of the cell types according to hematopoietic lineage. We explored whether this cord blood and the adult peripheral blood reference data sets impact the estimation of cell type composition in cord blood samples from an independent birth cohort (MoBa, n = 1092). This revealed significant differences for all cell types. Importantly, comparison of the cell type estimates against matched cell counts both in the cord blood reference samples (n = 11) and in another independent birth cohort (Generation R, n = 195), demonstrated moderate to high correlation of the data. This is the first cord blood reference data set with a comprehensive examination of the downstream application of the data through validation of estimated cell types against matched cell counts.

Assessing peripheral blood cell profile of Yorkshire pigs divergently selected for residual feed intake.

The cost of feed is a serious issue in the pork industry, contributing about 65 to 75% of the total production cost. To prevent economic losses and decreased productivity of the herd, it is important to select for animals that eat less for the same lean gain, or more efficient animals. Residual feed intake (RFI) is the difference between observed feed intake and expected feed intake based on estimated maintenance and production requirements. Selection for decreased RFI, or more efficient animals, is a potential solution to higher feed costs in pig production. However, animals that are highly selected for decreased RFI may have reduced energy input to the immune system and fail to withstand diseases and stressors after infection that negatively impact profitability. The objective of this study was to evaluate differences in circulating blood cell profiles at a young age between 2 lines of Yorkshire pigs that were divergently selected for RFI as well as the heritability of these traits, to investigate effects of selection for RFI on immune system parameters, and to identify potential biomarkers for feed efficiency. Previous work has shown that the 2 lines had diverged for IGF-1 in serum in young pigs and, therefore, this stage was investigated for other potential physiological differences. Blood samples were drawn for a complete blood count (CBC) analysis from 517 gilts and barrows, ages 35 to 42 d, across the 2 lines. In general, the low-RFI line had lower numbers of specific types of white blood cells but higher hemoglobin concentration and red blood cell volume compared to the high-RFI line. No significant correlations were found between CBC traits and RFI across and within the lines (0.05 < < 0.1). Of the 15 CBC traits that were measured, 3 were highly heritable (0.56 < < 0.62), 9 were moderately heritable (0.12 < < 0.47), and 3 were lowly heritable ( < 0.12), suggesting a substantial genetic component for CBC traits and that selection for CBC traits could be effective. Our results also show that selection for RFI has significantly impacted the number of circulating blood cells. In this experiment, we studied only healthy animals that were not under known pathogen challenge; therefore, our results cannot be directly applied to a disease challenge situation. Future work will be to challenge the animals and determine the effect of challenge on CBC levels.

Interlaboratory Reproducibility of Blood Morphology Using the Digital Microscope.

Differential counting of peripheral blood cells is an important diagnostic tool. However, manual morphological analysis using the microscope is time-consuming and requires highly trained personnel. The digital microscope is capable of performing an automated peripheral blood cell differential, which is as reliable as manual classification by experienced laboratory technicians. To date, information concerning the interlaboratory variation and quality of cell classification by independently operated digital microscopy systems is limited. We compared four independently operated digital microscope systems for their ability in classifying the five main peripheral blood cell classes and detection of blast cells in 200 randomly selected samples. Set against the averaged results, the R(2) values for neutrophils ranged between 0.90 and 0.96, for lymphocytes between 0.83 and 0.94, for monocytes between 0.77 and 0.82, for eosinophils between 0.70 and 0.78, and for blast cells between 0.94 and 0.99. The R(2) values for the basophils were between 0.28 and 0.34. This study shows that independently operated digital microscopy systems yield reproducible preclassification results when determining the percentages of neutrophils, eosinophils, lymphocytes, monocytes, and blast cells in a peripheral blood smear. Detection of basophils was hampered by the low incidence of this cell class in the samples.

Studies on morphology and cytochemistry in blood cells of ayu Plecoglossus altivelis altivelis.

Peripheral blood cells from ayu, Plecoglossus altivelis altivelis, were separated using a density gradient. Blood cells were then smeared using Shandon Cytospin and subjected to cytochemical staining. Blood cells were categorized based on morphological and cytochemical characteristics, and the density fractionation range and nucleus area/cell area ratio were observed. Lymphocytes are distinguished from neutrophils by their basophilic cytoplasm and Golgi-like field. The features of chromatin in thrombocytes are different from those of lymphocytes or neutrophils, but some small neutrophils have similar chromatin. Therefore, it is necessary to perform peroxidase staining to distinguish small neutrophils from thrombocytes. Basophils have large basophilic granules in cytoplasm. Based on density fractionation of blood cells, thrombocytes in the low-density area were separated from other blood cells. Identification of peripheral blood cells from ayu was possible with these staining methods. Monocytes/macrophages from spleen are specifically positive for esterase staining by α-naphthyl butyrate. As a result, thrombocytes, lymphocytes, neutrophils, basophils and monocytes/macrophages were identified in smears from peripheral blood or spleen tissue. In this paper, we confirmed that the peripheral blood corpuscles of ayu are able to be identified using the present staining methods.

Isolation and characterization of equine peripheral blood-derived multipotent mesenchymal stromal cells / Isolamento e caracterização das células mesenquimais multipotentes estromais derivadas do sangue periférico equino

The objective of the study was to isolate, cultivate and characterize equine peripheral blood-derived multipotent mesenchymal stromal cells (PbMSCs). Peripheral blood was collected, followed by the isolation of mononuclear cells using density gradient reagents, and the cultivation of adherent cells. Monoclonal mouse anti-horse CD13, mouse anti-horse CD44, and mouse anti-rat CD90 antibodies were used for the immunophenotypic characterization of the surface of the PbMSCs. These cells were also cultured in specific media for adipogenic and chondrogenic differentiation. There was no expression of the CD13 marker, but CD44 and CD90 were expressed in all of the passages tested. After 14 days of cell differentiation into adipocytes, lipid droplets were observed upon Oil Red O (ORO) staining. Twenty-one days after chondrogenic differentiation, the cells were stained with Alcian Blue. Although the technique for the isolation of these cells requires improvement, the present study demonstrates the partial characterization of PbMSCs, classifying them as a promising type of progenitor cells for use in equine cell therapy.

O objetivo deste estudo foi isolar, cultivar e caracterizar as células mesenquimais multipotentes estromais derivadas do sangue periférico (SpCTMs) equino. O sangue periférico foi coletado, seguido do isolamento das células mononucleadas utilizando o reagente de gradiente de densidade e o cultivo das células aderentes. Os anticorpos monoclonais mouse anti-horse CD13, mouse anti-horse CD44 e mouse anti-rat CD90 foram utilizados para a caracterização imunofenotípica da superfície das SpCTMs. Estas células também foram cultivadas utilizando meio de cultura específico para a diferenciação adipogênica e condrogênica. Não houve expressão do marcador CD13, mas os marcadores CD44 e CD90 foram expressos em todas as passagens testadas. Após 14 dias da diferenciação das células em adipócitos, gotículas de lipídeos foram observados através da coloração com Oil Red O. Vinte e um dias após a diferenciação condrogênica, as células foram coradas com o Alcian Blue. Embora a técnica de isolamento destas células necessite ser otimizada, o presente estudo demonstra a caracterização parcial das SpCTMs, classificando-as como um tipo de células progenitoras promissoras para o uso na terapia celular em equinos.

Feeding patterns of molestus and pipiens forms of Culex pipiens (Diptera: Culicidae) in a region of high hybridization.

BACKGROUND: Two biological forms of the mosquito Culex pipiens s.s., denoted pipiens and molestus, display behavioural differences that may affect their role as vectors of arboviruses. In this study, the feeding patterns of molestus and pipiens forms were investigated in Comporta (Portugal), where high levels of inter-form admixture have been recorded. METHODS: Indoor and outdoor mosquito collections were performed in the summer of 2010. Collected Cx. pipiens s.l. females were molecularly identified to species and form by PCR and genotyped for six microsatellites. The source of the blood meal in post-fed females was determined by ELISA and mitochondrial DNA sequencing. RESULTS: The distribution of the forms differed according to the collection method. The molestus form was present only in indoor collections, whereas pipiens and admixed individuals were sampled both indoors and outdoors. In both forms, over 90% of blood meals were made on avian hosts. These included blood meals taken from Passeriformes (Passer domesticus and Turdus merula) by females caught resting inside domestic shelters. CONCLUSION: Genetic structure and blood meal analyses suggest the presence of a bird biting molestus population in the study area. Both forms were found to rest indoors, mainly in avian shelters, but at least a proportion of females of the pipiens form may bite outdoors in sylvan habitats and then search for anthropogenic resting sites to complete their gonotrophic cycle. This behaviour may potentiate the accidental transmission of arboviruses to humans in the region.

Rapid blood meal scoring in anthropophilic Aedes albopictus and application of PCR blocking to avoid pseudogenes.

Blood meal analysis (BMA) is a useful tool for epidemiologists and vector ecologists to assess which vector species are critical to disease transmission. In most current BMA assays vertebrate primers amplify DNA from a blood meal, commonly an abundant mitochondrial (mtDNA) locus, which is then sequenced and compared to known sequences in GenBank to identify its source. This technique, however, is time consuming and costly as each individual sample must be sequenced for species identification and mixed blood meals cloned prior to sequencing. Further, we found that several standard BMA vertebrate primers match sequences of the mtDNA of the Asian tiger mosquito, Aedes albopictus, making their use for blood meal identification in this species impossible. Because of the importance of Ae. albopictus as a vector of dengue and chikungunya viruses to humans, we designed a rapid assay that allows easy identification of human blood meals as well as mixed meals between human and nonhuman mammals. The assay consists of a nested PCR targeting the cytochrome b (cytb) mtDNA locus with a blocking primer in the internal PCR. The blocking primer has a 3' inverted dT modification that when used with the Stoffel Taq fragment prevents amplification of nuclear cytochrome b pseudogenes in humans and allows for the continued use of cytb in BMA studies, as it is one of the most species-rich loci in GenBank. We used our assay to examine 164 blooded specimens of Ae. albopictus from suburban coastal New Jersey and found 62% had obtained blood from humans with 7.6% mixes between human and another mammal species. We also confirmed the efficiency of our assay by comparing it with standard BMA primers on a subset of 62 blooded Ae. albopictus. While this assay was designed for use in Ae. albopictus, it will have broader application in other anthropophilic mosquitoes.

Is peripheral blood cell balanced altered by the use of fresh frozen bone block allografts in lateral maxillary ridge augmentation?

BACKGROUND: The relationship between the immune response and red and white blood cell homeostasis is cited in literature, but no studies regarding the balance of these cell populations following maxillary bone-graft surgeries can be found. AIM: The aim of this study was to evaluate the possible impairments in the blood cell balance following fresh-frozen allogeneic bone-graft augmentation procedures in patients who needed maxillary reconstruction prior to implants. MATERIAL AND METHODS: From 33 patients elected to onlay bone grafting procedures, 20 were treated with fresh-frozen bone allografts and 13 with autologous bone grafts. Five blood samples were collected from each patient in a 6-month period (baseline: 14, 30, 90, and 180 days postsurgery), and the hematological parameters (erythrogram, leukogram, and platelets count) were accessed. RESULTS: All evaluated parameters were within the reference values accepted as normal, and significant differences were found for the eosinophils count when comparing the treatments (30 days, p = .035) and when comparing different periods of evaluation (allograft-treated group, baseline × 180 days, p ≤ .05 and 90 × 180 days, p ≤ .01; autograft-treated group, 30 × 90 days, p ≤ .05 and 30 × 180 days, p ≤ .05). CONCLUSIONS: Both autologous and fresh-frozen allogeneic bone grafts did not cause any impairment in the red and white blood cell balance, based on quantitative hemogram analysis, in patients subjected to maxillary reconstruction.