scholarly journals Heteroplasmy variability in individuals with biparentally inherited mitochondrial DNA

2020 ◽  
Author(s):  
Jesse Slone ◽  
Weiwei Zou ◽  
Shiyu Luo ◽  
Eric S Schmitt ◽  
Stella Maris Chen ◽  
...  

ABSTRACTWith very few exceptions, mitochondrial DNA (mtDNA) in humans is transmitted exclusively from mothers to their offspring, suggesting the presence of a strong evolutionary pressure favoring the exclusion of paternal mtDNA. We have recently shown strong evidence of paternal mtDNA transmission. In these rare situations, males exhibiting biparental mtDNA appear to be limited to transmitting just one of the mtDNA species to their offspring, while females possessing biparental mtDNA populations consistently transmit both populations to their offspring at a very similar heteroplasmy level. The precise biological and genetic factors underlying this unusual transmission event remain unclear. Here, we have examined heteroplasmy levels in various tissues among individuals with biparental inheritance. Our results indicate that individuals with biparental mtDNA have remarkable inter-tissue variability in heteroplasmy level. At the single-cell level, paternal mtDNA heteroplasmy in sperm varies dramatically, and many sperm possess only one of the two mtDNA populations originally in question. These results show a fundamental, parent-of-origin difference in how mtDNA molecules transmit and propagate. This helps explain how a single population of mtDNAs are transmitted from a father possessing two populations of mtDNA molecules, suggesting that some mtDNA populations may be favored over others when transmitted from the father.

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4231-4231
Author(s):  
Gillian A. Horne ◽  
Chinmay Rajiv Munje ◽  
Ross Kinstrie ◽  
Eduardo Gómez-Castañeda ◽  
Helen Wheadon ◽  
...  

Abstract The introduction of BCR-ABL tyrosine kinase inhibitors has revolutionized the treatment of chronic myeloid leukemia (CML). A major clinical aim remains the identification and elimination of low-level disease persistence, termed "minimal residual disease". Disease persistence suggests, that despite targeted therapeutic approaches, BCR-ABL-independent mechanisms exist which sustain the survival of a small population of cells, termed leukemic stem cells (LSC). We previously identified CD93 expression as a promising biomarker of LSC in chronic phase (CP)-CML. Our group has described the long term self-renewal potential of Lin-CD34+93+ CP-CML cells compared to their Lin-CD34+93- counterparts through LTCIC assays (n=3, p<0.0001) and NSG engraftment models (3.5-30-fold increased in engraftment with Lin-CD34+93+ cells, p<0.03). We hypothesized that CD93+-selected cells would represent a more immature functional phenotype compared to CD93- selected cells. The aim of this study was to characterize differences in the gene expression profile between CD93+ and CD93- CML LSC populations and determine heterogeneity of each population at a single cell level. To interrogate this, we initially identified CP-CML subpopulations with the greatest functional capability compared to normal. Normal and CP-CML samples were FACS-sorted into HSC/LSC, CMP, GMP, and MEP sub-populations. Results suggest a significant change in functional status between normal and CP-CML subpopulations within the HSC/LSC compartment (lin-CD34+CD38-CD45RA-CD90+), where CML LSC demonstrated significantly increased proliferation (14 fold expansion; P<0.001) compared to normal HSC (no expansion) after 5 days in vitro culture in physiological growth factors. In addition, equivalent numbers of CML LSC produce ~4-fold more colonies in colony forming cell (CFC) assays than normal HSC (329±56 versus 86±17 per 2,000 cells, respectively (p<0.05)). Furthermore, fluorescence in situ hybridization demonstrated that >90% of lin-CD34+CD38-CD45RA-CD90+ CML LSC from all patient samples were BCR-ABL positive. Subsequent experiments were confined to the LSC population. We hypothesized that lin-CD34+CD38-CD90+CD93- CML cells would have a more mature gene expression profile compared to lin-CD34+CD38-CD90+CD93+ cells. CP-CML cells were sorted into (1) lin-CD34+, (2) lin-CD34+CD38-CD90+CD93- and (3) lin-CD34+CD38-CD90+CD93+ populations. RNA was harvested at baseline from bulk populations (1) to (3) and cDNA was generated from single cells using the Fluidigm C1 autoprep system. Using Fluidigm technology, quantitative PCR of 90 lineage-specific and cell survival genes was performed within all populations of cells (1) to (3) in 'bulk' samples (n=3), and at single cell level (n=123 CD93+, n=120 CD93-single cells; n=3 samples in total). Bulk sample analysis demonstrated a significant increase in expression of lineage commitment genes within the lin-CD34+CD38-CD90+CD93- population, as shown by increased expression of GATA1 (p=0.0007), and CBX8 (p=0.0002). The lin-CD34+CD38-CD90+CD93+ population displayed a less lineage-restricted profile with increased expression of CDK6 (p=0.05), HOXA6 (ns), CDKN1C (ns) and CKIT (p=0.0014), compared to the lin-CD34+CD38-CD90+CD93- population. Furthermore, the two populations could be segregated by differential gene expression through gene clustering. At a single cell level, differences were noted in the frequency of expression between lin-CD34+CD38-CD90+CD93- and lin-CD34+CD38-CD90+CD93+ populations, particularly in GATA1, TPOR, and VWF. Although a statistically significant change was demonstrated in gene expression between the lin-CD34+CD38-CD90+CD93- and lin-CD34+CD38-CD90+CD93+ populations in a number of genes, we were not able to segregate the populations by differential expression using gene clustering. This highlights the heterogeneous nature of the cell populations and the inability to distinctly characterize between the two populations at a single cell level. Our results validate CD93 as a potential biomarker to separate the primitive CP-CML LSC population and highlight key lineage and cell survival pathways that are altered in CML LSC. The results demonstrate the heterogeneity seen within gene expression at the single cell level, which may allow for further insight into the CML LSC compartment with further analyses. Disclosures Wheadon: GlaxoSmithKline: Research Funding. Copland:Shire: Honoraria; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; ARIAD: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria.


2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Bianka Mussil ◽  
Rodolphe Suspène ◽  
Vincent Caval ◽  
Anne Durandy ◽  
Simon Wain-Hobson ◽  
...  

2019 ◽  
Author(s):  
Ruixin Wang ◽  
Dongni Wang ◽  
Dekai Kang ◽  
Xusen Guo ◽  
Chong Guo ◽  
...  

BACKGROUND In vitro human cell line models have been widely used for biomedical research to predict clinical response, identify novel mechanisms and drug response. However, one-fifth to one-third of cell lines have been cross-contaminated, which can seriously result in invalidated experimental results, unusable therapeutic products and waste of research funding. Cell line misidentification and cross-contamination may occur at any time, but authenticating cell lines is infrequent performed because the recommended genetic approaches are usually require extensive expertise and may take a few days. Conversely, the observation of live-cell morphology is a direct and real-time technique. OBJECTIVE The purpose of this study was to construct a novel computer vision technology based on deep convolutional neural networks (CNN) for “cell face” recognition. This was aimed to improve cell identification efficiency and reduce the occurrence of cell-line cross contamination. METHODS Unstained optical microscopy images of cell lines were obtained for model training (about 334 thousand patch images), and testing (about 153 thousand patch images). The AI system first trained to recognize the pure cell morphology. In order to find the most appropriate CNN model,we explored the key image features in cell morphology classification tasks using the classical CNN model-Alexnet. After that, a preferred fine-grained recognition model BCNN was used for the cell type identification (seven classifications). Next, we simulated the situation of cell cross-contamination and mixed the cells in pairs at different ratios. The detection of the cross-contamination was divided into two levels, whether the cells are mixed and what the contaminating cell is. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the segmentation model DialedNet was used to present the classification results at the single cell level. RESULTS The cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet. The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines). DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%. CONCLUSIONS This study successfully demonstrated that cell lines can be morphologically identified using deep learning models. Only light-microscopy images and no reagents are required, enabling most labs to routinely perform cell identification tests.


RSC Advances ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 5384-5392
Author(s):  
Abd Alaziz Abu Quba ◽  
Gabriele E. Schaumann ◽  
Mariam Karagulyan ◽  
Doerte Diehl

Setup for a reliable cell-mineral interaction at the single-cell level, (a) study of the mineral by a sharp tip, (b) study of the bacterial modified probe by a characterizer, (c) cell-mineral interaction, (d) subsequent check of the modified probe.


2021 ◽  
Vol 22 (11) ◽  
pp. 5988
Author(s):  
Hyun Kyu Kim ◽  
Tae Won Ha ◽  
Man Ryul Lee

Cells are the basic units of all organisms and are involved in all vital activities, such as proliferation, differentiation, senescence, and apoptosis. A human body consists of more than 30 trillion cells generated through repeated division and differentiation from a single-cell fertilized egg in a highly organized programmatic fashion. Since the recent formation of the Human Cell Atlas consortium, establishing the Human Cell Atlas at the single-cell level has been an ongoing activity with the goal of understanding the mechanisms underlying diseases and vital cellular activities at the level of the single cell. In particular, transcriptome analysis of embryonic stem cells at the single-cell level is of great importance, as these cells are responsible for determining cell fate. Here, we review single-cell analysis techniques that have been actively used in recent years, introduce the single-cell analysis studies currently in progress in pluripotent stem cells and reprogramming, and forecast future studies.


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