scholarly journals Single-cell Sequencing-based Pre-implantation Genetic Testing-M (PGT-M) of the Heterozygous Mutations of PKD1 Gene

2020 ◽  
Author(s):  
Na Li ◽  
Cong Xiu Miao ◽  
HuiLing Bi ◽  
Hui Miao ◽  
Dan Li ◽  
...  
Keyword(s):  
Genetic Testing ◽  
Single Cell ◽  
Sanger Sequencing ◽  
Genetic Diseases ◽  
Copy Number Variant ◽  
Final Diagnosis ◽  
Mutation Site ◽  
Single Cell Sequencing ◽  
Pkd1 Gene ◽  
Important Approach

Abstract Objectives: Polycystic kidney disease (PKD) is a common autosomal monogenic genetic disease. PKD1 mutation accounts for about 85% of ADPKD patients. Pre-implantation genetic testing-M (PGT-M for monogenic) is an important approach to prevent the transmission of genetic diseases from parents to the offspring. Design: In this study, We have identified the family linkage and mutation site in embryos with NGS-based SNP phasing and Sanger Sequencing.Methods: Multiple Annealing and Looping Based Amplification Cycles (MALBAC) method was employed to amplify the whole genome of trophoblast cells. Copy Number Variant (CNV), and single nucleotide polymorphism (SNP) were used to assess the embryo state. Results: In the eight embryos, Embryo 02 and Embryo 04 were removed from further analysis because of the Multiple chromosomes abnormal (2 of 8, 25%). Embryo 05, Embryo 06, Embryo 07, and Embryo 08 were judged as 46,XN,-15q(q23→qter,~31M,×1,mos*), 45,XN,-16(×1), 47,XN,+2(×3),-7p(pter→p14.3,~35M,×1,mos*), and 46,XN, +16(×3,mos*),-20p(pter→p11.23,~23M,×1,mos*),+22(×3,mos*), respectively (4 of 8, 50%). Meanwhile, Embryo 01 and Embryo 03 were judged as 46, XN (2 of 8, 25%). The results of SNP phasing and Sanger Sequencing suggested that Embryo 01 and Embryo 05 had none of PKD1 gene mutation. Limitations: Up to now, PGT-M is complicated and expensive. Meanwhile, PGT-M obtains the final diagnosis through invasive manipulation of embryos, which may bring adverse effects on offspringConclusion: NGS-based single-cell sequencing combined with CNV, Sanger Sequencing, and SNP phasing is a reliable testing system for PGT-M application. This work presented here would provide a detailed understanding of the NGS-based single-cell sequencing application in ADPKD.

scholarly journals Single-Cell Sequencing Confirms Transcripts and VHDJH Rearrangements of Immunoglobulin Genes in Human Podocytes

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 472
Author(s):  
Zhenling Deng ◽  
Huige Yan ◽  
Zhan Shi ◽  
Xinyu Tian ◽  
Zhuan Cui ◽  
...  
Keyword(s):  
Single Cell ◽  
Sanger Sequencing ◽  
Single Cell Level ◽  
Normal Kidney ◽  
Cell Level ◽  
Glomerular Diseases ◽  
Single Cell Sequencing ◽  
Heavy Chains ◽  
Single Cell Rna Sequencing ◽  
Healthy Control

Most glomerular diseases are associated with inflammation caused by deposited pathogenic immunoglobulins (Igs), which are believed to be produced by B cells. However, our previous study indicated that the human podocyte cell line can produce IgG. In this study, we aimed to confirm the transcripts and characterize the repertoires of Igs in primary podocytes at single cell level. First, single-cell RNA sequencing of cell suspensions from “normal” kidney cortexes by a 10xGenomics Chromium system detected Ig transcripts in 7/360 podocytes and Ig gene segments in 106/360 podocytes. Then, we combined nested PCR with Sanger sequencing to detect the transcripts and characterize the repertoires of Igs in 48 single podocytes and found that five classes of Ig heavy chains were amplified in podocytes. Four-hundred and twenty-nine VHDJH rearrangement sequences were analyzed; podocyte-derived Igs exhibited classic VHDJH rearrangements with nucleotide additions and somatic hypermutations, biased VH1 usage and restricted diversity. Moreover, compared with the podocytes from healthy control that usually expressed one class of Ig and one VHDJH pattern, podocytes from patients expressed more classes of Ig, VHDJH patterns and somatic hypermutations. These findings suggested that podocytes can express Igs in normal condition and increase diversity in pathological situations.

scholarly journals Phenotypic signatures in clinical data enable systematic identification of patients for genetic testing

2020 ◽  
Author(s):  
Theodore J Morley ◽  
Lide Han ◽  
Jonathan Morra ◽  
Nancy J Cox ◽  
Lisa Bastarache ◽  
...  
Keyword(s):  
Genetic Testing ◽  
Clinical Data ◽  
Genetic Disease ◽  
Genetic Test ◽  
Genetic Diseases ◽  
Copy Number Variant ◽  
Test Sample ◽  
Chromosomal Microarray ◽  
Quickest Path ◽  
Organ Systems

Around five percent of the population is affected by a rare disease, most often due to genetic variation. A genetic test is the quickest path to a diagnosis, yet most suffer through years of diagnostic odyssey before getting a test, if they receive one at all. Identifying patients that are likely to have a genetic disease and therefore need genetic testing is paramount to improving diagnosis and treatment. While there are thousands of previously described genetic diseases with specific phenotypic presentations, a common feature among them is the presence of multiple rare phenotypes which often span organ systems. Here, we hypothesize that these patients can be identified from longitudinal clinical data in the electronic health record (EHR). We used diagnostic information from the EHRs of 2,286 patients that received a chromosomal microarray and 9,144 matched controls to train and test a prediction model. We identified high prediction accuracy (AUROC = 0.97, AUPR = 0.92) in a held-out test sample and in 172,265 hospital patients where cases were defined broadly as interacting with a genetics provider (AUROC = 0.9, AUPR = 0.63). High probabilities (median = 0.97) were associated with 46 patients carrying a known pathogenic copy number variant (CNV) among a subset of 6,445 genotyped patients. Our model identified many more patients needing a genetic test while increasing the proportion having a putative genetic disease compared to the current nonsytematic approach. Taken together, we demonstrate that phenotypic patterns representative of a genetic disease can be captured from EHR data and provide an opportunity to systematize decision making on genetic testing to speed up diagnosis, improve care, and reduce costs.

scholarly journals From Oncogenic Signaling Pathways to Single-Cell Sequencing of Immune Cells: Changing the Landscape of Cancer Immunotherapy

Molecules ◽  
2021 ◽  
Vol 26 (8) ◽  
pp. 2278
Author(s):  
Afshin Derakhshani ◽  
Zeinab Rostami ◽  
Hossein Safarpour ◽  
Mahdi Abdoli Shadbad ◽  
Niloufar Sadat Nourbakhsh ◽  
...  
Keyword(s):  
Single Cell ◽  
Signaling Pathways ◽  
Immune Cells ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Cancer Development ◽  
Immune Checkpoints ◽  
Single Cell Sequencing ◽  
Increased Risk

Over the past decade, there have been remarkable advances in understanding the signaling pathways involved in cancer development. It is well-established that cancer is caused by the dysregulation of cellular pathways involved in proliferation, cell cycle, apoptosis, cell metabolism, migration, cell polarity, and differentiation. Besides, growing evidence indicates that extracellular matrix signaling, cell surface proteoglycans, and angiogenesis can contribute to cancer development. Given the genetic instability and vast intra-tumoral heterogeneity revealed by the single-cell sequencing of tumoral cells, the current approaches cannot eliminate the mutating cancer cells. Besides, the polyclonal expansion of tumor-infiltrated lymphocytes in response to tumoral neoantigens cannot elicit anti-tumoral immune responses due to the immunosuppressive tumor microenvironment. Nevertheless, the data from the single-cell sequencing of immune cells can provide valuable insights regarding the expression of inhibitory immune checkpoints/related signaling factors in immune cells, which can be used to select immune checkpoint inhibitors and adjust their dosage. Indeed, the integration of the data obtained from the single-cell sequencing of immune cells with immune checkpoint inhibitors can increase the response rate of immune checkpoint inhibitors, decrease the immune-related adverse events, and facilitate tumoral cell elimination. This study aims to review key pathways involved in tumor development and shed light on single-cell sequencing. It also intends to address the shortcomings of immune checkpoint inhibitors, i.e., their varied response rates among cancer patients and increased risk of autoimmunity development, via applying the data from the single-cell sequencing of immune cells.

Single-cell sequencing technology in tumor research

2021 ◽  
Author(s):  
Xue Bai ◽  
Yuxuan Li ◽  
Xuemei Zeng ◽  
Qiang Zhao ◽  
Zhiwei Zhang
Keyword(s):  
Single Cell ◽  
Sequencing Technology ◽  
Single Cell Sequencing ◽  
Tumor Research

scholarly journals Red panda: a novel method for detecting variants in single-cell RNA sequencing

BMC Genomics ◽  
2020 ◽  
Vol 21 (S11) ◽  
Author(s):  
Adam Cornish ◽  
Shrabasti Roychoudhury ◽  
Krishna Sarma ◽  
Suravi Pramanik ◽  
Kishor Bhakat ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Articular Chondrocytes ◽  
Genetic Diseases ◽  
Simulated Data ◽  
Single Nucleotide ◽  
Single Cell Rna Sequencing ◽  
Red Panda ◽  
Novel Method ◽  
Rare Cells

Abstract Background Single-cell sequencing enables us to better understand genetic diseases, such as cancer or autoimmune disorders, which are often affected by changes in rare cells. Currently, no existing software is aimed at identifying single nucleotide variations or micro (1-50 bp) insertions and deletions in single-cell RNA sequencing (scRNA-seq) data. Generating high-quality variant data is vital to the study of the aforementioned diseases, among others. Results In this study, we report the design and implementation of Red Panda, a novel method to accurately identify variants in scRNA-seq data. Variants were called on scRNA-seq data from human articular chondrocytes, mouse embryonic fibroblasts (MEFs), and simulated data stemming from the MEF alignments. Red Panda had the highest Positive Predictive Value at 45.0%, while other tools—FreeBayes, GATK HaplotypeCaller, GATK UnifiedGenotyper, Monovar, and Platypus—ranged from 5.8–41.53%. From the simulated data, Red Panda had the highest sensitivity at 72.44%. Conclusions We show that our method provides a novel and improved mechanism to identify variants in scRNA-seq as compared to currently existing software. However, methods for identification of genomic variants using scRNA-seq data can be still improved.

Sign in / Sign up

Export Citation Format

Share Document