This program shows how large data sets can be analysed with CyDAS for recurrentgains and losses as well as recurrent break points. Data from many sourcescan be analysed, if they were exported into a text file in the Mitelmanformat (or directly downloaded from the Mitelman database; see HowToDownload data from the Mitelman database) or special other formatsfor karyotype banding analysis or comparative genome hybridisation (CGH)(see HowTo on Data Formats).
Data from many sources can be analysed, if they were exported into atext file in the Mitelman format (or directly downloaded from the Mitelmandatabase; see HowTo Download data fromthe Mitelman database) or special other formats for karyotype bandinganalysis or comparative genome hybridisation (CGH) (see HowToon Data Formats).
Karyotype Analysis Software Free Download
To accelerate karyotype studies in plants only few software programs are available, including MicroMeasure (Reeves and Tear 2000), IdeoKar (Mirzaghaderi and Marzangi 2015) and KaryoType (Altınordu et al. 2016). These programs allow measurement of chromosome parameters such as centromere index, arm length and ratio, asymmetry index, etc. However, none of these programs is able to simultaneously measure chromosome parameters and chromosomal landmark positions (e.g. band, FISH and GISH signals), allowing idiogram construction.
Specialized cytogenetics labs are equipped with the necessary infrastructure, including highly sophisticated software and instruments specifically set up for the purpose of karyotype analysis and chromosome painting, such as, for example, Isis (Metasystems), CytogeneticsTM (Leica Biosystems), and Hyperspectral (Applied Spectral Imaging). However, if this method needs to be established and used as a side project in a lab not thus equipped, this might turn into a challenge: while imaging facilities are typically accessible, software packages come at a cost that may not be budgeted for given the restricted budgets of most core facilities.
Karyotyping was performed independently by two individuals using two cell culture systems. After cell culture and sample preparation, a LABB M9120 instrument (Shanghai Beion Medical Technology, Shanghai, China) and matching image analysis software were used for chromosome karyotype scanning and analysis. At least three cell karyotypes were analyzed for each culture, and 20 karyotypes were counted. For the cases with chromosome mosaicism, more karyotypes were counted or analyzed. Karyotyping and descriptions were based on the International Human Cytogenomic Naming System (2020) [15].
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To download these files into the appropriate ChAS library folder from within the software, use the Help>Update Library and Annotation Files functionality or download them from the Analysis Workflow using Utilities>Download Library Files. Within RHAS you can download them from Preferences>Download Library Files.
Alternatively, to copy the files into your ChAS Library folder manually, download the Analysis Files.zip to the data analysis workstation, extract the zip archive, open the folder containing the files, copy all of the files, and paste them into the ChAS Library, using the instructions in the ChAS User Guide located in the ChAS software zip package.
Abstract:By way of a Next-Generation Sequencing NGS high throughput approach, we defined the mutational profile in a cohort of 221 normal karyotype acute myeloid leukemia (NK-AML) enrolled into a prospective randomized clinical trial, designed to evaluate an intensified chemotherapy program for remission induction. NPM1, DNMT3A, and FLT3-ITD were the most frequently mutated genes while DNMT3A, FLT3, IDH1, PTPN11, and RAD21 mutations were more common in the NPM1 mutated patients (p
To distinguish between these explanations and to examine karyotype dynamics in chromosome instable lymphoma, we use a newly developed single-cell whole genome sequencing (scWGS) platform that provides a complete and unbiased overview of copy number variations (CNV) in individual cells. To analyse these scWGS data, we develop AneuFinder, which allows annotation of copy number changes in a fully automated fashion and quantification of CNV heterogeneity between cells. Single-cell sequencing and AneuFinder analysis reveals high levels of copy number heterogeneity in chromosome instability-driven murine T-cell lymphoma samples, indicating ongoing chromosome instability. Application of this technology to human B cell leukaemias reveals different levels of karyotype heterogeneity in these cancers.
Our data show that even though aneuploid tumours select for particular and recurring chromosome combinations, single-cell analysis using AneuFinder reveals copy number heterogeneity. This suggests ongoing chromosome instability that other platforms fail to detect. As chromosome instability might drive tumour evolution, karyotype analysis using single-cell sequencing technology could become an essential tool for cancer treatment stratification.
Indeed, karyotype heterogeneity occurs in human cancers [16, 17], has been associated with human tumour evolution, and might therefore impact therapeutic response to cancer therapy [18]. However, most current cytogenetic and molecular techniques are limited in the number of karyotype alterations per cell they can detect, are biased towards dividing subpopulations, or can only measure the population-average chromosome copy number alterations [19, 20]. These shortcomings have precluded thorough analysis of intratumour chromosome copy number variations. Recent advances in single-cell genomics allow researchers to dissect the heterogeneity of the cancer genome with greater resolution than ever before [21, 22].
Chromosomal instable T-ALL display recurring chromosome copy numbers, as assessed by array CGH. a Two representative T-ALLs analysed using array CGH, compared to a euploid reference, showing recurrent gains of chromosomes 4, 9, 14 and 15, and other tumour-specific alterations. The purple bars indicate the mean log-value of the respective chromosome. b Cumulative single-cell sequencing libraries to simulate bulk data, showing a comparable karyotype as found by aCGH. c Single-cell sequencing analysis of four representative cells from T-ALL 1, showing identical chromosome copy numbers to the aCGH profile (cell 1), or cell-unique copy numbers (cells 2, 3 and 4; red arrows)
Further analysis of the 4n population of T158 revealed that 12 out of 37 analysed cells had an odd copy number for at least one of the chromosomes, indicating that at least 32 % of the 4n cells were true near-tetraploid and not G2 cells. The heterogeneity score for these near-tetraploid cells (0.6503) was much larger than for the 2n population (0.3099), indicating that the near-tetraploid cells can generate greater karyotype diversity than near-diploid cells, presumably because of the larger number of copy number states available. For the other 4n cells (68 %) we could not discriminate whether these were G2 cells or G1 near-tetraploid cells in this experimental setup. However, the 4n population only represented 4.1 % of all the live tumour cells, and therefore only between 1.3 % (32 % of 4.1 % near-tetraploid cells with odd numbers of chromosomes) and 4.1 % (all cells with near 4n DNA content) in tumour T158 were near-tetraploid cells. The contribution of these cells to intratumour heterogeneity is therefore limited and in agreement with the observed cell death events in the time-lapse experiments in primary aneuploid T-ALL cultures (Additional file 14: Movie 4). In the T257 4n population (5.3 % of all tumour cells) we found two out of 34 analysed cells to have odd chromosome numbers, representing 0.3 % of the total tumour. Therefore, the contribution of genuine near-tetraploid cells to tumour T257 lies between 0.3 % and 5.3 %. We conclude that while the near-tetraploid cells contribute to intratumour heterogeneity, this contribution is limited, presumably due to cell death events after polyploidisation leading to low fractions of near-tetraploid cells in the primary aneuploid T-ALLs. 2ff7e9595c
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