The mutations observed in iPSC lines affect a higher proportion of mtDNA molecules, favouring non-synonymous protein-coding and tRNA variants, including known disease-causing mutations. Analysing 11,538 single cells shows stable heteroplasmy in sub-clones derived from the original donor during differentiation, with mtDNA variants influencing the expression of key genes involved in mitochondrial metabolism and epidermal cell differentiation. Thus, the dynamic mtDNA landscape contributes to the heterogeneity of human iPSCs and should be considered when using reprogrammed cells experimentally or as a therapy.Individual induced pluripotent stem cells (iPSCs) show considerable phenotypic heterogeneity, but the reasons for this are not fully understood. Comprehensively analysing the mitochondrial genome (mtDNA) in 146 iPSC and fibroblast lines from 151 donors, we show that most age-related fibroblast mtDNA mutations are lost during reprogramming. However, iPSC-specific mutations are seen in 76.6% (108/141) of iPSC lines at a mutation rate of 8.62 × 10 Here the authors describe high depth mitochondrial DNA (mtDNA) sequence analysis of 146 human induced pluripotent stem cell (hiPSC) lines as well as single cell RNA-seq (scRNAseq) of hiPSCs undergoing differentiation from 125 donors; reporting mtDNA diversity and some variants favoured after reprogramming. Even iPSCs derived from the same donor cell line show phenotypic differences that are poorly understood. However, independently differentiating the same cell line several times reproduces similar cell phenotypes4, implicating cell-intrinsic factors driving the heterogeneity, rather than the external environment.There is a growing interest in the use of induced pluripotent stem cells (iPSCs) to model human disease mechanisms and in cell-based therapies, but iPSCs derived from the same tissue show considerable phenotypic heterogeneity that can affect their capacity to differentiate into organ-specific lineages. Bulk and single-cell analysis has shown common genetic variation contributes to differences in gene expression profiles between donors5. Most human cells contain >1000 copies of mtDNA, and deep sequencing has shown that most humans harbour a mixed population of mitochondrial genomes (heteroplasmy)6. mtDNA mutations can arise de novo during life or be inherited down the maternal line, and specific mtDNA variants compromise oxidative metabolism and the synthesis of adenosine triphosphate (ATP). High percentage levels of specific mtDNA mutations cause severe multi-system metabolic diseases that affect ~1 in 8000 humans, but they also contribute to the pathology of common age-related disorders, including Parkinson’s disease5. mtDNA mutations influence canonical cell signalling pathways, reactive oxygen species production, amino acid metabolism and cell growth through indirect effects on nuclear gene transcription10.Although there has been considerable effort describing nuclear genetic and epigenetic differences that arise during reprogramming, the 16.5 Kb mitochondrial genome (mtDNA) remains largely uncharacterised. mtDNA codes for 13 essential peptide components of the oxidative phosphorylation systems, and 2 rRNAs required for intra-mitochondrial protein synthesis11, including mitochondrial respiration in derived cardiomyocytes12. However, with <25 donors studied to date, the landscape of mtDNA variation in human iPSCs poorly understood13.mtDNA mutations also accumulate in somatic tissues throughout life, raising the possibility that mtDNA variation contributes to the molecular heterogeneity of human iPSCs. In keeping with this, several small studies have described mtDNA variants present in iPSC lines that were not detected in parental fibroblast lines, with some having detrimental effects on metabolismIn this work, we analysed high-depth mtDNA sequences in 146 iPSC lines as part of the Human-Induced Pluripotent Stem Cells Initiative (HipSci). We show extreme mtDNA diversity, with selection favouring some variants during reprogramming. Single-cell analysis shows that mtDNA variants define sub-clones that modulate gene expression within iPSCs and subsequent differentiated cell lineages.http://www.hipsci.org/). After sample quality control (QC), 141 iPSC lines and 146 fibroblast lines were included in the analysis (see 'Methods'), with one iPSC line from 25 donors and 2 iPSC lines from 58 donors included  . We filtered out calls likely to be due to systematic errors, including nuclear-encoded mitochondrial DNA sequences (NUMTs) and length variants flanking polynucleotide tracts (see 'Methods'). We then determined the frequency distribution of major population-specific mtDNA haplogroups (macro-haplogroups) , confirming that the vast majority of donors were representative of the United Kingdom population. In total, 143 of the 146 donors (98%) belonged to one of the macro-haplogroups H, I, J, K, T, U, V, or W. As a further QC step, we only included heteroplasmic variants with bulk heteroplasmy allele fractions (HFs) between 2 and 98% (see 'Methods').Initially, we analysed high-depth mtDNA sequences derived from whole-genome sequence (WGS) data on 146 iPSC and 151 fibroblast lines obtained from 151 different donors aged 27–77 years recalled through the NIHR BioResource as part of the Human Induced Pluripotent Stem Cells Initiative   data generated from 102 iPSCs (see 'Methods'). As expected, the depth of RNA-seq was less uniform than WGS   , but there was a neither detectable correlation with age seen for the other genomic regions nor across the whole mtDNA genome   of 146 fibroblast lines carried at least one heteroplasmy on bulk analysis (HF > = 2%). The mean number of heteroplasmic mtDNA variants per fibroblast line was 8 (s.d. = 4), with a greater mean number in fibroblasts from older individuals (l) Figs. , where tel) Fig. . Overallel) Fig. .Fig. 2Ch16. The previously observed m.2623G and m.13369C variants13 were also seen in 12.3%, despite being exceptionally rare in blood from the same population6. These mutations occurred on different mtDNA haplogroup backgrounds, indicating recurrent mutation events.Eight heteroplasmic variants were present in >5% of the fibroblast lines Figs. . m.414G Each mutation initially affects a single mtDNA molecule, but to be detected by our WGS pipeline, at least 2% of molecules must be affected in the bulk DNA sample . Thus, the mutations we observed are a consequence of the original mutation event followed by subsequent copying  and propagation of the variant into many daughter cells. Although it is conceivable that some high-level heteroplasmic variants could reflect recurrent mutations in the same individual, this seems unlikely because the majority (64.5%) of heteroplasmic variants were seen in only one fibroblast line. Clonal expansion must therefore be playing a key role in generating the higher heteroplasmy values seen in fibroblasts from older subjects, and explain the correlation between the HF and age for m.414G Fig. .Next, we carried out a similar analysis of high-depth mtDNA sequences in 141 iPSCs derived from 83 of the original fibroblast lines Fig. . In contP = 6.38 × 10−20, paired Wilcoxon test)  . There was no detectable difference in either the mean HF, the mean number of heteroplasmic variants, or the mean number of iPSC-specific mutations in iPSC lines analysed at different passages   Fig. . In totast) Fig. . By contst) Fig. , making −3/base pair, D-loop vs. expected P = 2.03 × 10−16, 95% CI 1.84–2.63, Fisher’s exact test). The D-loop had a higher mutation frequency than all of the other mtDNA regions  . By contrast, in the iPSCs, the mutation frequency in the D-loop was lower than the rest of the mtDNA     Fig. . Heteropst) Fig. , and ovest) Fig. . In keepst) Fig. . Taken tst) Fig.  with thest) Fig. .Fig. 3Th−5 per base pair per genome per reprogramming , which is a conservative estimate based on mutations with a HF > =2% in the iPSCs and a HF > 0 in their matched fibroblast cell lines. Finally, to determine whether our initial detection threshold had a major impact on the measured iPSC-specific mutation rate, we decreased the detection threshold in iPSCs by 0.5% increments. This showed the anticipated increased mutation rate, but only by approximately fourfold, indicating that our approach is reliable to within one order of magnitude  of the unique heteroplasmic variants present in each donor fibroblast were detected in both iPSC lines (shared both); 11.0% (49/436) were present in one of the two iPSC lines (shared one/lost one); and 80.6% (351/436) were not detected in either iPSC line (lost from both). By contrast, 78.0% (184/236) of the unique heteroplasmic variants in each donor iPSC line were only present in one of the two iPSCs (iPSC-specific one) and 6.8% (16/236) in both iPSCs (iPSC-specific both) Fig. . Thus, t18. A similar genetic bottleneck effect could explain why variants not detectable in fibroblasts become detectable in iPSCs. However, given the inherent inefficiency of cellular reprogramming, an alternative explanation is that there is a ‘cellular bottleneck’, where only a small proportion of the fibroblasts contribute to the iPSC cell population. In keeping with this, the average amount of mtDNA per cell was greater in iPSCs than in the matched fibroblast lines   Fig. , althougP = 4.75 × 10−14, shared vs iPSC-specific in iPSCs P < 2.2 × 10−16, Wilcoxon rank-sum test) . Note that this correction reflects the fold change in HF, but the size of this change is influenced by the initial HF.As expected, the HF for shared heteroplasmic variants found in both the fibroblasts and iPSCs lines was greater than HF seen for lost and iPSC-specific heteroplasmic variants (shared vs lost in fibroblasts st) Fig. , in keepst) Fig.    , and the HSs for non-synonymous and tRNA variants were more likely to increase than other regions   Fig. . Of 161 P = 1.15 × 10−3, coefficient estimate = 0.78, s.d. = 0.24, tRNA P = 4.93 × 10−3, coefficient estimate = 0.89, s.d. = 0.32, logistic regression model)  of a variant as a function of the age of the individual when the skin biopsy was taken, the HF in the original fibroblast line, the mitochondrial genome location, and the macro-haplogroup of the donor (see 'Methods'). Variants in the D-loop region were less likely to increase than variants in non-synonymous and tRNA regions (non-synonymous el) Fig. . Thus, m6. Thus, tissue or context-specific factors influence the segregation of mtDNA heteroplasmy in humans, implying a more complex mechanism than simply the effect of the variants on oxidative phosphorylation and ATP synthesis. This is in keeping with work in mice, where pluripotent cells modulate heteroplasmy in a different way to mouse embryonic fibroblasts (MEFs), and reprogramming MEFs to iPSCs recapitulates the pluripotent cell behaviour19. For the D-loop, one potential explanation involves the selection against variants that compromise mtDNA replication, given the increased mtDNA content seen in iPSC lines. To gain insight into the selection of the D-loop region, we further defined the heteroplasmic variants in different D-loop regions  were more likely to be lost than expected when compared to the average across D-loop region , particularly in L-strand promoter (MT-LSP)  which generates the RNA–primer required for DNA synthesis and includes the age-related mutation m.414G. By contrast, iPSC-specific mutations were enriched in MT-OHR , particularly in MT-CSB1 . Although it is difficult to explain all of these patterns at present, their distribution is likely to reflect the function of D-loop structures.The preferential loss of D-loop variants and propagation of non-synonymous and tRNA variants are the complete opposite of what is seen during germline transmissionons Fig. . VariantOf the 12 known pathogenic mtDNA mutations present in 9 donor fibroblast lines Figs.  and 3d, −6 per base pair per genome (95% CI 7.88 × 10−7–5.31 × 10−6 per base per genome) . Accounting for differences in the definition of a pathogenic mutation, the iPSC-specific mutation rate during iPSC reprogramming was ~5.9-fold greater than estimated for the germ line6.Five iPSC-specific pathogenic mutations were not seen in the fibroblast lines from four donors, giving a iPSC-specific pathogenic mutation rate of 2.14 × 1021, next we determined the mutational signature of the heteroplasmic mtDNA variants. As expected, in fibroblasts, C > T and T > C substitutions were the most common. We did not observe the L-strand T > C mutation bias seen in whole blood and 18 cancer types20 , with more C > A, C > G, T > A variants  and less T > C variants  than donor fibroblasts , and C > G substitutions were strongly enriched on the H-strand  , with the shared and lost variants having the similar signature profile   Fig. . The L-sst) Fig. 20. iPSC-es) Fig. . Thus, c 24 Fig. , althougn = 9661), mesoderm , definitive endoderm  and undefined cells (n = 6278)3 (see 'Methods'). We studied 11,538 single cells  from 60 donors. The bulk WGS data of 60 progenitor fibroblast cells were also available and included in our analysis above. In total, 6288 of 11,538 single cells from 36 iPSC lines also had the bulk WGS data from the same iPSC lines. In all, 5607 cells from 30 iPSC lines had matched bulk RNA-sequencing data  from 125 donors at three canonical stages of endoderm differentiation: iPSCs    (see 'Methods'). Overall, 1647 pseudo-bulk variants (HF > 0 at the pseudo-bulk level) were observed across the whole dataset Fig. . There wNext, we studied mtDNA heteroplasmy levels during differentiation at the single-cell level. We only included the cell lines where at least 20 cells were available at each stage of development to minimise sampling bias. We estimated the proportion of cells that carried the same variant within each cell line, the mean HF per variant within each cell line, and pseudo-bulk HF at all three stages Fig. . In all,P < 2.2 × 10−16R2 = 0.90, iPSC vs defendo P < 2.2 × 10−16R2 = 0.92, iPSC vs mesendo P < 2.2 × 10−16R2 = 0.88, Pearson’s correlation test)  Fig. , with a 25 based solely on the mtDNA heteroplasmic variant data  ata Fig. , revealiata Fig.  (see 'Mest) Fig. . The numst) Fig. , with sist) Fig. . This inst) Fig. , which w3. However, analysing the mean HF of non-synonymous mtDNA variants uncovered additional differences in gene expression attributable to the mtDNA variants. In the 12 tested cell lines, mtDNA variants accounted for >5% of the variance in expression for 897 genes in one of three developmental stages. Up to 129 genes in an individual line showed differential expression attributable to non-synonymous mtDNA variants . As seen previously, the cell developmental stage was the main source of variation, followed by the experimental batch and cell line of origin  was greater, and variants in different mtDNA regions were subject to purifying selection or were enriched through a selective advantage. We also saw iPSC-specific mtDNA mutations emerging in individual iPSC lines which displayed a distinct mutational signature, and known pathogenic mtDNA mutations reached high percentage heteroplasmy levels. These findings are in keeping with previous observations in human embryonic stem cells (hESCs) which also harbour a wide range of different mtDNA mutations at the single-cell level13 in this context. For this reason, we have not referred to these variants as ‘de novo’, because we cannot be certain that these variants are not present in a very small number of molecules in the original fibroblast line. A more sensitive detection technique might reveal ultra-rare mutations in fibroblasts that are present in iPSCs at much higher levels, lowering the iPSC-specific mutation rate. However, it is also likely that a more sensitive technique would detect additional mutations in iPSCs not present in the fibroblasts, counterbalancing this effect. At this point, we can only speculate what the implications of a more sensitive detection method might be. However, this does not change our main conclusion that the heteroplasmy levels change during cell reprogramming. A re-analysis of published data13 from human 30 iPSC clones derived from three blood samples yields a very similar mutation rate (8.85 × 10−5/base pair) to our result calculated from the reprogramming of human fibroblasts (8.62 × 10−5/base pair). This adds weight to our conclusion that reprogramming shapes the mtDNA landscape, and appears to be independent of the original cell type. However, definitive evidence will require a much more extensive comparison of iPSCs derived from different tissues from the same individual.It is important to note that, as with any sequencing experiment, there are technical limitations to our approach. For example, we are unable to completely exclude rare variants in the fibroblast lines that fell well below the detection threshold of 2% used in the bulk WGS, either because they were at low heteroplasmy levels in many cells, or because a very small number of cells carried homoplasmic variants. Technical limitations in the sequencing approach, such as the introduction of base errors, mean that we are unable to absolutely exclude the possibility of any variant present in iPSCs being present in fibroblasts, and our reported iPSC-specific mutation rate must be interpreted27, packaging of mtDNA into ‘segregating units’ which reduced the effective population size of mtDNA molecules28, the focal replication of mtDNA in different regions of the cell29, and related mechanisms involving the fission and fusion of mitochondria30. A bottleneck effect could also occur at the cellular level due to the preferential replication or loss of cells containing specific mtDNA variants during reprogramming. This is supported by recent evidence in mice, where mtDNA variants have been shown to influence reprogramming efficiency from fibroblasts to iPSCs19.What mechanisms could explain the changing heteroplasmy levels we have observed during reprogramming? Rapid shifts in heteroplasmy could be due to a genetic bottleneck effect. This has been extensively studied in the female germline where the bottleneck could be due to a reduction in mtDNA content within the cell31. At the cellular level, differences in cellular proliferation or cell death could influence heteroplasmy levels in the whole population of cells32. Selection could also occur at the mitochondrial level, through increased biogenesis or selective destruction by autophagy or mitophagy33. Some mtDNA variants could also influence mtDNA replication itself, particularly those in the non-coding D-loop. Given emerging evidence that cell culture conditions such as the oxygen level of the addition or antioxidants can influence heteroplasmy levels34, including during iPSCs generation19, it is possible that the precise conditions used to generate the iPSCs influenced the heteroplasmy dynamics that we observed. However, the HipSci resource provides an opportunity to study all of these mechanisms now that the mtDNA variation has been characterised in detail.The selection could occur at several levels3, the mtDNA defined sub-clones defined within each lineage showed differences in gene expression, including key genes involved in cellular metabolism, including ATP synthesis, telomere maintenance and organisation and epidermal cell differentiation. Most of the variants (93.7%) influencing gene expression were from iPSC/mesendo/defendo-specific variants during reprogramming. Thus, the process of cell reprogramming shapes the mtDNA landscape clone-by-clone, adding to the heterogeneity of derived iPSCs. It is highly likely that these findings will have an impact on iPSC disease models, and the use of iPSC cell lines in cellular therapies. These cell lines are available to the scientific community through the human induced pluripotent stem cell initiative (https://www.hipsci.org/)1.Analysing single-cell transcriptomes at three states of differentiation allowed the identification of distinct cell lineages based on their mtDNA sequence. Unlike the reprogramming, heteroplasmy levels remained stable during the three stages of differentiation. Although only making a minor contribution to the overall variance in gene expression between donorshttp://www.hipsci.org/)1. The donors’ age was between 27 and 77 years . Samples were collected initially under ethical approval for iPS cell derivation , with later samples collected under a revised consent .In all, 146 iPSC and 151 fibroblast lines from 151 healthy donors  were obtained from the Human Induced Pluripotent Stem Cells Initiative ). On reaching confluence after ~30 days, fibroblasts were passaged into 25-cm2 flasks, and further passaged when 80–90% confluent into a 75-cm2 flask. Cells were then expanded to confluency in 225-cm2 flasks at a split ratio of 1:3, and cryopreserved at 1–2 million cells per vial in FBS and 10% DMSO or seeded immediately for reprogramming. Fibroblasts were transduced using Sendai vectors expressing human (h)OCT3/4, hSOX2, hKLF4 and hMYC51  and cultured on an irradiated mouse embryonic fibroblast (MEF-CF1) feeder layer in a 10-cm2 tissue-culture dish in iPSC medium consisting of advanced DMEM (Life Technologies) supplemented with 10% Knockout Serum Replacement , 2 mM l-glutamine (Life Technologies), 0.007% 2-mercaptoethanol (Sigma-Aldrich), 4 ng ml−1 recombinant zebrafish fibroblast growth factor-2, and 1% pen/strep (Life Technologies). Cells with an iPS cell morphology appeared ~25–30 days after transduction. Six undifferentiated colonies per donor selected between days 30–40 were transferred onto 12-well MEF-CF1 feeder plates and cultured in iPS cell medium with daily medium change, and passaged every 5–7 days. Three of the six lines were selected based on morphological qualities  and expanded for banking and characterisation. Each iPS cell line was passaged ~16 times before the initial molecular data for quality control, including genotyping, gene expression data, and an assessment of the pluripotency and differentiation potential of each line.The iPSCs were generated in bulk culture without physical subcloning. Details of the generation of the iPSC lines including fibroblast isolation, iPSC derivation, iPSC culture, iPSC line selection, molecular assays and cell culture for maintenance and differentiation in scRNA sequencing are available at A summary of the iPSC lines used in this study is available in Supplementary Data http://www.hipsci.org/). The average depth of WGS was 44-fold (s.d. = 9-fold)  on the resulting smaller BAM files to generate the realigned mtDNA BAM files36. During this process, any read pairs for which either of the two reads in the pair mapped to multiple locations were discarded, including potential nuclear mitochondrial sequences in the nuclear genome and amplification artefacts. The realigned bam files were used to call the variants and generate the vcf file for each sequence. We also used the second variant caller VarScan237 to call mtDNA variants from the realigned bam file . The mpileup files used in VarScan2 were generated by Samtools with options -d 0 -q 30 -Q 30. We then filtered the variants as follows: (1) we retained variants for which the allele fractions (AFs) were above 2% from both MToolbox and VarScan2, and the allele fractions were extracted from VarScan2. The average sequencing depth of mtDNA was 1824-fold (s.d. 2249-fold), which ensured us to detect the low-level variants; (2) we retained only single-nucleotide polymorphisms (SNPs); (3) we removed variants with depth < 200×; (4) we removed variants where there were less than two reads on each strand for the minor allele; (5) we removed variants falling within low-complexity regions .Whole-genome sequencing (WGS) data of 146 iPSC and 151 fibroblast lines were obtained from HipSci  was based on our previous work40.Mitochondrial DNA haplogroup assignment was performed using variants with allele fraction above 95% by HaploGrep2Potential DNA cross-contamination was checked using mtDNA variant calls. Two samples carrying more than ten heteroplasmic variants with similar HFs were excluded from this study. We also removed four sequences where the average depth of mtDNA was below 400×. A further four iPSCs were removed because their matched fibroblasts were unavailable or failed quality controls. After all of the sample QC steps, 146 fibroblasts and 141 iPSCs were included in the analysis. From 25 donors one iPSC line was included and from 58 donors 2 iPSC lines were included Fig. .P = 0.907, paired Wilcoxon test). Likewise, the average number of heteroplasmic variants, the heteroplasmy fraction (HF), and the average number of low-level heteroplasmic variants (HF < 5% or HF < 10%) were no detectable difference between the two clusters   shared, if the variant was present in both fibroblasts and their derived iPSCs and was heteroplasmic in at least one of the variants; (2) lost, if the heteroplasmic variant was present in the fibroblast but not detected in their derived iPSC and (3) iPSC-specific, if the heteroplasmic variant was present in the iPSC and not detected in their fibroblast progenitor. If two iPSCs were derived from the same fibroblast, we defined them as two separate fibroblast and iPSC pairs.Next, we defined sub-categories of heteroplasmic variants detected in the fibroblast cell lines with two derived iPSCs and their derived iPSCs: (1) shared both, if the variant was present in the fibroblast and both derived iPSCs and was heteroplasmic in at least one of the variants; (2) shared one/lost one, if the variant was present in the fibroblast and only one of two iPSCs and was heteroplasmic in at least one of the variants; (3) iPSC-specific both, if the same heteroplasmic variant was present in both iPSCs and not detected in their matched fibroblast; (4) iPSC-specific one, if the heteroplasmic variant was present in only one of two iPSCs and not detected in their matched fibroblast; (5) lost both, if the heteroplasmic variant was present in the fibroblast but not detected in any of their derived iPSCs.41.To understand the relationship between donor age and the mean number of heteroplasmic variant and average heteroplasmy fraction in fibroblast cell lines and iPSC lines, we applied linear regression to each dataset using RS = 1 if the normalised heteroplasmic shift was above 0, and S = 0 if normalised heteroplasmic shift was below 0, HFvfibro was the logit transformed heteroplasmy fraction from each variant in the fibroblast cell lines, Rmt was the mtDNA regions , Hap was the macro-haplogroup from each donor, and age was the donor age.To understand whether multiple factors affect the heteroplasmic shifts between fibroblast cell lines and iPSC lines, logistic regression was applied using R.42 and the previous study38. We then reviewed each variant by searching online literatures. Based on previously published criteria43, a total of 87 single-nucleotide mutations were included in the final list of pathogenic mutations44.The initial list of pathogenic mutations was from MitoMap (Disease Mutations with mutation status as ‘Cfrm’)24. The substitution rate for each trinucleotide context was estimated by the number of substitutions divided by the frequency of the trinucleotide context present in the mtDNA reference genome (rCRS), in the light (L) and heavy (H) strands. The signature profiles from the total heteroplasmic variants observed in fibroblasts and iPSCs, lost heteroplasmic variants in fibroblasts, iPSC-specific heteroplasmic variants in iPSCs and shared heteroplasmic variants between fibroblasts and iPSCs were considered. To compare the signature profiles between the different mtDNA regions, we estimated the frequency of six point mutations in the L and H strands. To explore the mechanism of mutagenesis we correlated the signature profiles of mtDNA substitutions from fibroblasts, iPSCs, shared and iPSC-specific heteroplasmic variants with 30 cancer-specific mutational signatures in the nuclear DNA identified24.Mutational spectra were derived from the revised Cambridge Reference Sequence (rCRS) and alternative alleles at each variant site. The resulting spectra were composed of the six distinguishable point mutations . Each signature was displayed using a 96 substitution classification defined by the substitution class and the sequence context immediately 3’ and 5’ of the mutated baseWe estimated the relative mtDNA copy number from WGS by comparing the mean read depth of the autosomes to the mean depth of mtDNA. Mean autosomal depth (DPautosome) was calculated from 2.685 Gb autosome regions (without chromosome gaps). And mean mitochondrial depth (DPmt) was obtained from the full length of mtDNA genome . Relative mtDNA copy number (CNmt) was calculated in a diploid cell as below:45.Bulk RNA-seq FASTQ files for 332 iPSC lines were obtained from the ENA project: ERP007111 and EGA projects: EGAS00001000593. Raw sequencing data were used to detect mitochondrial variants using the same pipeline as described above. Variants with low-quality RNA-seq were discarded based on the criteria applied in WGS data. In addition, we removed the highly heteroplasmic variants that were specific to mtRNA , including three variants  previously reported as artefacts . 102 of 332 iPSC lines that had matched WGS data available were included in this studyhttps://zenodo.org/record/3625024#.Xil-0y2cZ0s. The details of cell culture and single-cell sequencing were previously described3. The pipeline developed for the WGS data was used to detect mtDNA variants from raw FASTQ files, with several additional filtering steps: (1) retaining the cells with mtDNA genome sequencing depth above 200×, (2) excluding the iPSC/mesendo/defendo-specific variants only detected in a single cell from the same cell line which was more likely from sequencing error, (3) defining heteroplasmic variants with HF between 2% and 95%, due to lower sequencing depth in single-cell RNA-sequencing data compared to WGS, (4) excluding the cells with >20 heteroplasmic variants, (5) retaining the mtDNA regions with mean sequencing depth above 200× . This resulted in 11, 538 cells from 60 cell lines which also had WGS data available from their matched fibroblast cell lines.Single-cell RNA-seq FASTQ files for 36,044 cells were obtained from the ENA project: ERP016000 and EGA projects: EGAS00001002278 and EGAD00001005741. Processed single-cell count data were obtained from Zenodo: https://zenodo.org/record/3625024#.Xil-0y2cZ0s. The stage labels post iPSC (mesendo and defendo) were defined using the methods previously described3. Briefly, cells were assigned to the mesendo stage if they were collected at day 1 or day 2 and had pseudotime values between 0.15 and 0.5. Cells were assigned to the defendo stage if they were collected at day 2 or day 3 and had pseudotime values higher than 0.7. Cells with intermediate pseudotime (between 0.5 and 0.7) were assigned as undefined. Overall, we assigned 9969 (86.4%) cells to any of the stages .The pseudotime of the single cells from single-cell RNA-seq was obtained from Zenodo: To compare the variant profiles between three cell stages, we included the cell lines where at least 20 cells were available at each stage of development to minimise sampling bias, which included 6881 cells from 24 cell lines. The average allele frequency of each variant was estimated across all the cells from the same cell line at each cell stage, which we termed pseudo-bulk variants.25 of the cells from the different cell lines and within each cell line using the mitochondrial DNA variants described above. UMAP was analysed using the UMAP package46 with default parameters in R and visualised using the M3C package47 in R.For the analysis of the cell lineages, we only included 692 variants detected in more than 1% cells within the same cell line from 24 cell lines which had >20 cells assigned to each cell stage. In all, 27 of 692 variants were also present in the matched fibroblast cell lines, and 665 were iPSC/mesendo/defendo-specific variants in the single cells. We performed a Uniform Manifold Approximation and Projection (UMAP)48. Variance component analysis was performed on top 4000 most variable genes using a linear mixed model in the variancePartition R package49. We included the cell stage, the experiment and the cell line identity as random effects. To estimate the effects of mtDNA variants on gene expression, we identified top 4000 most variable genes in the cells from the same cell stage, the same experiment and the same cell line identity. Variance component analysis was performed by partitioning gene expression variation into mean heteroplasmy fractions of mtDNA non-synonymous variants and residual noise.We only included the cells and variants meeting the following filtering criteria: 1) cells were assigned to one of three cell stages , (2) cell lines with at least 100 cells and >20 cells assigned to each cell stage and (3) retaining variants where >15 cells carried one of two alleles in each cell stage  enrichment analysis was performed using Enrichr53.To further understand the effect of each mtDNA variant on gene expression, we tested the differences in gene expression between cells carrying different genotypes of mtDNA variants from the same cell line. We fitted a generalised linear model for the gene expression data within each cell line from the same experiment with the cell stages as covariates using MAST R package41 and Python (http://www.python.org). Figures were generated using Matplotlib (https://matplotlib.org) in Python and R. Circos plots were made using Circos54.All statistical analyses in this study were performed using RFurther information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1-5


They induced the accumulation of a comparable amount of a subG1 population, but slightly lower extent of caspase activation, as compared with actinomycin D, used as an apoptosis inducer. The quantitative structure–activity relationship analysis suggests the significant correlation of tumor-specificity with a 3D shape of molecules, and possible involvement of inflammation and hormone receptor response pathways. Conclusions: Compounds 7 and 15 can be potential candidates of a lead compound for developing novel anticancer drugs.Background: Very few papers covering the anticancer activity of azulenes have been reported, as compared with those of antibacterial and anti-inflammatory activity. This led us to investigate the antitumor potential of fifteen 4,6,8-trimethyl azulene amide derivatives against oral malignant cells. Methods: 4,6,8-Trimethyl azulene amide derivatives were newly synthesized. Anticancer activity was evaluated by tumor-specificity against four human oral squamous cell carcinoma (OSCC) cell lines over three normal oral cells. Neurotoxicity was evaluated by cytotoxicity against three neuronal cell lines over normal oral cells. Apoptosis induction was evaluated by Western blot and cell cycle analyses. Results: Among fifteen derivatives, compounds  Azulene is a 10 π-electron non-benzenoid aromatic hydrocarbon with a fused structure of five- and seven-membered rings, showing a deep blue coloration. The resonance structure of azulene contains ionic cyclopentadienide and tropylium substructures, resulting in electrophilic substitution reactions at the 1- and 3-positions and nucleophilic addition reactions at the 2-, 4-, 6-, and 8-positions, along with the 2-position at the five-membered ring in some cases   × 100] .The cells were washed, lysed, and their protein extracts subjected to Western blot (WB) analysis, as described previously . All pro6 cells) were fixed with paraformaldehyde  in PBS(−) and treated with ribonuclease (RNase) A (Sigma-Aldrich). After staining with propidium iodide  in the presence of 0.01% Nonidet-40  to prevent cell aggregation, the cells were filtered through Falcon® cell strainers  and then subjected to cell sorting , as described previously [Cells (approximately 10eviously . Cell cyeviously .50) was used for the comparison of the cytotoxicity between the compounds, since the CC50 values had a distribution pattern close to a logarithmic normal distribution. The mean pCC50 values for normal cells and tumor cell lines were defined as N and T, respectively [50 against OSCC), N represents −log . The chemical structures were cleaned and standardized ). Then, the 3D-structure of each compound was generated by CORINA Classic  and determined optimal 3D-structure with force field calculations (amber-10: EHT) in Molecular Operating Environment (MOE) version 2020.09 . Chemical 2D and 3D descriptors were calculated with MOE version 2020.9 and Mordred version 1.2.0 (Python library) [pCC50   based onThe activities against 59 signaling pathways , agonistt-test was performed using JMP Pro 6 . The significance level was set at p < 0.05.Each experimental value was expressed as the mean ± standard deviation (SD) of triplicate or quadruplicate measurements. One-way ANOVA and Dunnett’s post-test were performed using IBM SPSS 27.0 statistics . The correlation between chemical descriptors and cytotoxicity or tumor specificity was investigated using simple regression analyses by scikit-learn and SciPy with Python 3.8.5. Student’s 1. The 3D-structure may be the determinant of tumor-selectivity. There was possible involvement of inflammation and hormone receptor pathway rather than caspase pathway in the selective cytotoxicity against OSCC cells. At present, TS values of compounds 7, 9, and 15 are not so high compared with anticancer drugs (9 was cytotoxic, further chemical modification of this compound that enhances the TS value should be done. On the other hand, compound 7, compound 15, and 5-FU showed cytostatic growth inhibition, and their action may be potentiated through combination with other types of anticancer drugs [The present study demonstrated that three compounds from fifteen 4,6,8-trimethyl azulene amide derivatives selectively inhibited the growth of human oral squamous cell carcinoma cell lines. Their actions are either cytotoxic or cytostatic, accumulating subGer drugs . Since cer drugs .


N-isopropylacrylamide)n-block-poly((3-acrylamidopropyl) trimethylammonium chloride)m (PNIPAAMn-b-PAMPTMA(+)m) can efficiently deliver DNA and siRNA to HeLa [n:m) [n-b-PAMPTMA(+)m copolymers by investigating their cytotoxicity in human cancer cell lines and in a normal breast cell line.Thermoresponsive polymers have been widely investigated in modern drug delivery and gene delivery systems, due to their potential to load and release the payload by simply modifying local temperature . Previou to HeLa  and HT10La [n:m) ,3. In ann:m ratio 48:6, 48:10 and 65:20) were dissolved in varying concentrations in cell culture medium, as described before [Cell lines: HeLa , HCC1806 (breast cancer) and MCF10A .The copolymers (n-b-PAMPTMAm(+) copolymers was shown to be dependent on the polymer concentration, n:m ratio and cell line. MCF10A cells were very tolerant to the presence of the copolymer 48:6, even at the highest concentration (Discussion and conclusions: The low toxicity shown particularly for the non-tumorigenic cell line but higher toxicity towards the cancer cell lines makes the tested copolymers highly appealing as drug carriers in anti-cancer therapy.In this preliminary study the cytotoxicity of the PNIPAAMntration , followe


Patients with esophageal malignancy have a 5-year survival rate of only 14% in Canada. This high mortality rate is due to three factors: late diagnosis, difficulty to surgically remove the tumor due to its localization and treatment resistance. Treatment resistance has been ascribed to the presence of cancer stem cells (CSCs) inside the tumor. However, no treatment specifically directed against CSCs is available to patients. Therefore, targeting CSCs is a promising strategy to improve survival of patients with esophageal squamous cell carcinoma (ESCC), the most common type of esophageal cancer worldwide.Herein, we developed an unbiased approach to identify new players in chemotherapy and radiotherapy resistance in ESCC.high cells and CD24high/CD44high cells in flow cytometry. We then used a proteomic approach to identify new players in treatment resistance.We established radioresistant (RR), chemoresistant (CR) and radiochemoresistant (RCR) human ESCC cell lines using weekly radiation and/or continuous treatment with increasing doses of chemotherapeutic agent 5-FU. We validated that the process of resistance acquisition correlates with enrichment in CSCs as revealed by higher ALDH1 expression, and increased proportion of ALDH1high/CD24high cell proportion is decreased in control and RR cell lines suggesting that proline is a key regulator of CSC population in ESCC.Interestingly, pathway analysis demonstrated enrichment in energy metabolism as well as amino acid metabolism. Seahorse assays showed a more quiescent metabolism in all three types of resistant cells compared to the control cell line. More precisely, resistant cell lines have a lower respiration rate than control cell line, while glycolysis remains unchanged. Surprisingly, our results show a metabolic rewiring very different from the well-known Warburg effect. To further characterise these metabolic changes, we performed an unbiased metabolomic pilot study and confirmed a decrease in amino acid levels such as proline, in resistant cell lines. Preliminary data show that when cultured in DMEM with proline addition, CD44To conclude, our results suggest an important role of metabolism in ESCC treatment resistance. This study is a first step towards the identification of new targets to fight treatment resistance in ESCC patients.CAG, CIHRCanada research chair TIER 2


Drosophila. This screen disrupted the function of approximately 3500 genes and identified 137 candidate genes for which loss of function leads to observable changes in the hematopoietic development. Targeting RNAi to maturing, progenitor, and regulatory cell types identified key subsets that either limit or promote blood cell maturation. Bioinformatic analysis reveals gene enrichment in several previously uncharacterized areas, including RNA processing and export and vesicular trafficking. Lastly, the participation of students in this course-based undergraduate research experience (CURE) correlated with increased learning gains across several areas, as well as increased STEM retention, indicating that authentic, student-driven research in the form of a CURE represents an impactful and enriching pedagogical approach.Undergraduate students participating in the UCLA Undergraduate Research Consortium for Functional Genomics (URCFG) have conducted a two-phased screen using RNA interference (RNAi) in combination with fluorescent reporter proteins to identify genes important for hematopoiesis in  Biomedical Research 10H (formerly Life Sciences 10H). Since that time, the URCFG has conducted several large-scale genetic research projects that have yielded publishable data and research resources  was established at UCLA in 2003 as an entity representing the collaborative research effort of undergraduates, typically first- and second-year students, participating in a discovery-based laboratory course called Drosophila melanogaster. Over the last two decades, the fly has become an increasingly popular model for investigating the molecular mechanisms regulating blood cell specification, development, and function  in the fruit fly, Drosophila  occurs in two separate specification events that differ in space and time. The first wave of hematopoiesis occurs in the embryonic head mesoderm and creates blood cells that quickly mature and migrate throughout the developing embryo, eventually becoming the circulating blood cells of the larva. A subset of these cells, many of which appear to retain progenitor characteristics, become sessile, attaching to the lateral body wall around the chordotonal organs and to various internal organs  give rise to blood progenitors that belong to either myeloid or lymphoid lineage, which further differentiate into a variety of mature forms . Likewisl organs . The secl organs , 2015.Drosophila has three defined terminally differentiated blood cell types called plasmatocytes, crystal cells, and lamellocytes  region, from which early blood cells are derived, both require FGF, BMP, and Wnt signaling .P]S}{Hand-GAL4 Hml Chr.  was used as previously described .For the primary screen (expression throughout the hematopoietic system), the Drosophila RNAi Center , the National Institute of Genetics , and the Bloomington Drosophila Stock Center . Acquired RNAi lines were randomly assigned to students participating in the primary screen and the secondary screen, and each RNAi line was assigned to a minimum of two students. Each RNAi line was continually screened until two complete data sets (see below) were acquired. For target gene validation, the BDSC was searched for alternate RNAi lines targeting 24 candidate genes identified by Δ-GAL4Hml in our secondary screen (those causing strong increases in Δ-DsRedHml fluorescence); 14 alternative RNAi lines were available, obtained, and screened .Transgenic RNAi lines for screening were obtained from the Vienna GAL4 females were crossed to males from individual UAS-hpRNA lines or to males from 1118w (BDSC 5905) as a control. Crosses to HHLT-GAL4 and Δ-GAL4Hml were reared at 29°C to maximize RNAi-based phenotypes. Crosses to Antp-GAL4 and PG14-GAL4dome were placed directly at 29°C or reared for one day at 18°C before shifting to 29°C. Crosses to Antp-GAL4 and MESO-GAL4dome with elav-GAL80 were reared at room temperature for one day before shifting to 29°C.Virgin GAL4 driver line, larvae were sometimes prescreened for the presence of GFP and DsRed expression. Four immobile larvae were aligned dorsal side up along the anterior/posterior axis on the bottom (flat surface) of a glass spot well plate that was chilled on ice. Larvae were then imaged for GFP or DsRed fluorescence using a Zeiss Stemi SV11 fluorescence stereo dissection microscope  equipped with an AxioCam MRm camera, controlled by Zeiss AxioVision imaging software. Imaging 12 larvae (three sets of four larvae) for each cross was considered as a complete dataset.Wandering third-instar larvae (non-Tb) were collected, washed with water, and placed into glass spot well plates (Fisher) on ice to minimize movement. Depending upon balancer chromosomes present in the parental HHLT-GAL4 UAS-GFP) screen, students noted changes to fluorescence associated with the lymph gland region, including the posterior pericardial cells, and the circulating blood cell population. Changes noted were varying levels of increased or decreased fluorescence for lymph glands , whether pericardial cells were absent, increased or decreased circulating cell density (including clumps and melanotic tumors). For the secondary screen with Δ-DsRedHml as a marker, students noted changes to fluorescence associated with the lymph gland region and the circulating blood cell population. Changes noted were varying levels of increased or decreased fluorescence for lymph glands  and increased or decreased circulating cell density (including clumps and melanotic tumors). RNAi phenotypes were scored by two or more students in both the primary and the secondary screens, with “hits” being selected by causing reproducible phenotype scores at each stage. Because circulating cell phenotypes varied in several ways, scoring was more subjective. Thus, RNAi line reproducibly causing circulating cell phenotypes were consolidated into a single group that cause any relative change (Supplementary Table S3).Reporter gene expression (fluorescence) in progeny larvae activating RNAi within the hematopoietic system was compared with that of progeny larvae in which RNAi was absent (from control crosses). For the primary , 46.1% male (n = 142); of 294 respondents, 10.1% were URM (n = 31), where URM includes American Indian/Alaskan Native, Black/African American, or Hispanic/Latinx; student make-up by year: first-year, 33.1% (n = 102), second-year, 41.6% (n = 128), third-year, 20.8% (n = 64), and fourth-year, 4.5% (n = 14). SURE II survey participants (January 2015 through May 2018) identified as follows: of 17,810 respondents, 64.6% were female , 35.4% were male (n = 6298); of 17,638 respondents, 17.8% were URM (n = 3142); of 17,328 respondents, student make-up by year: first-year, 35.2% (n = 6103), second-year, 26.2% (n = 4547), third-year, 20.2% (n = 3496), and fourth-year, 18.4% (n = 3182). UCLA student demographic data were obtained under UCLA IRB#16-001388.Learning gains were assessed using the Survey of Undergraduate Research Experiences (SURE) II , which oGAL4 driver lines are available upon request from the Biomedical Research Minor and Banerjee laboratory (UCLA). RNAi lines are available from the Bloomington Drosophila Stock Center , NIG-FLY, National Institute of Genetics (Japan), and the Vienna Drosophila Resource Center (Austria). Supplementary Figure S1 shows phenotypic validation data for a subset of RNAi lines. Supplementary Table S1 contains a list of additional RNAi lines added to the collection identified by the primary phase of the genetic screen. Supplementary Table S2 lists the duplicate screening completion rate for each GAL4 driver/RNAi line combination. Supplementary Table S3 lists candidate genes regulating circulating blood cells. Supplementary Table S4 list Gene Ontology (GO) terms enriched among genes identified in the primary phase screen with HHLT-GAL4. Supplementary Table S5 lists all enriched Reactome groups among genes identified in the primary phase screen with HHLT-GAL4. Supplementary Figure S1 is in TIF format. All Supplementary Tables are in Microsoft Excel (.xlsx) format and have been uploaded to figshare: https://doi.org/10.25387/g3.13166891.GAL4/UAS gene expression system derived from yeast  in whole animals using fluorescence microscopy. An overview of the experimental design is shown in To identify hematopoietic genes, 339 URCFG students used RNA interference (RNAi) to disrupt the function of approximately 3500 genes within the developing blood system. In our experimental approach, pseudo-double-stranded hairpin RNAs (hpRNAs) are produced within cells from a transgene containing an inverted-repeat DNA sequence corresponding to a specific target gene  nor what the specific impact was on blood lineage development. To address these limitations and further delineate the functions of the identified candidate genes, the secondary screen was conducted in which RNAi was directed to either differentiating cells using the Δ-GAL4Hemolectin  that target either the primary screen candidate genes redundantly (20 genes) or genes predicted to function in related processes or pathways. Over the course of five academic quarters (Winter 2015–Spring 2016), students crossed RNAi lines from the 202-line collection with the three GAL4 drivers described above and analyzed DsRed fluorescence (Δ-DsRedHml expression) in whole, wandering third-instar larvae. Each RNAi line was assigned to two or more students, with the goal of collecting at least two complete data sets for each GAL4 driver/RNAi line cross combination. The collection of imaging data for 12 progeny larvae from a given cross was considered a complete data set, and individual RNAi lines remained within the assignment pool until two complete image data sets were obtained. The duplicate completion rate for the entire RNAi line collection was 41% (83 lines) for all three GAL4 drivers, 78% (158 lines) for at least two of three GAL4 drivers, and 95% (191 lines (95%) for at least one GAL4 driver. If single-complete data sets are included, the completion rate increases to 75% (151 lines) across all three drivers, to 99% (199 lines) for at least two of three GAL4 drivers, and to 100% (202 lines) for at least one GAL4 driver (Supplementary Table S2). With respect to RNAi in the PSC . Of the 180 viable lines, 160 (79%) were completed in duplicate. For RNAi screening in progenitor cells , students successfully screened 186 RNAi lines , of which 137 (68%) were completed in duplicate. Similar screening in the maturing blood cell population (Δ-DsRed HmlΔ-GAL4Hml) was successful for 182 RNAi lines , 135 (67%) of which were completed in duplicate (Supplementary Table S2).We compiled a collection of 202 RNAi lines comprised of the 148 lines identified in the primary screen, as well as 54 lines (Δ-DsRedHml reporter expression between RNAi and control (non-RNAi) backgrounds as viewed in whole, third instar larvae. While this is a highly specific and, therefore, powerful molecular genetic tool, the usefulness of Δ-DsRedHml in this RNAi screen is offset by variability in lymph gland phenotypes, possibly due to incomplete phenotypic penetrance and/or expressivity, within the 12 RNAi-larvae sample group. Additionally, the relative inexperience of the undergraduate researchers, with Drosophila in general and the hematopoietic system in particular, sometimes made their identification of subtle phenotypic changes difficult. Therefore, to increase the likelihood that RNAi lines (candidate genes) are identified correctly, the developmental phenotype caused by each RNAi line was independently scored by two or more students. Scoring consisted of first determining whether a phenotype was present and, if so, then describing and categorizing the nature of the phenotype. RNAi lines identified more than once and causing similar hematopoietic phenotypes (the majority of lines identified) were subdivided into those causing an increase in lymph gland Δ-DsRedHml expression and those causing a decrease in lymph gland Δ-DsRedHml expression. Though not our focus, changes in Δ-DsRedHml expression among circulating cells were also noted .As described previously, phenotypic analysis in the secondary screen involved discerning the variance of Antp-GAL4 identified 20 RNAi lines (representing 19 genes) that cause an increase in Δ-DsRedHml lymph gland fluorescence and 15 RNAi lines (representing 15 genes) that cause a decrease in Δ-DsRedHml lymph gland fluorescence (see Supplementary Table S3). Of these 13 RNAi lines, three overlap with RNAi lines increasing lymph gland DsRed fluorescence and four overlap with RNAi lines decreasing lymph gland DsRed fluorescence. The use of dome-GAL4 to disrupt gene function in lymph gland progenitor cells identified 34 RNAi lines (representing 33 genes) increasing lymph gland Δ-DsRedHml expression and 18 RNAi lines (representing 17 genes) decreasing lymph gland Δ-DsRedHml expression (see Supplementary Table S3), with six lines in common with those increasing lymph gland DsRed fluorescence and six lines in common with those decreasing lymph gland DsRed fluorescence. Lastly, analysis of RNAi lines in maturing/mature cells using Δ-GAL4Hml identified 50 RNAi lines (representing 48 genes) causing an increase in lymph gland Δ-DsRedHml expression and 8 RNAi lines (representing 8 genes) causing a decrease in lymph gland Δ-DsRedHml expression (see Supplementary Table S3), with 27 lines overlapping with those causing an increase in lymph gland Δ-DsRedHml fluorescence and four lines overlapping with those causing a decrease in lymph gland Δ-DsRedHml fluorescence.Directing RNAi to the PSC using ence see . This ansion see . Anothersion see . A totalΔ-GAL4Hml) using screen RNAi lines, cause an increase in lymph gland Δ-DsRedHml fluorescence. Subsequent analysis showed that 11 of 14 validation RNAi lines caused either the same or a highly similar phenotype as the original RNAi line . Several such RNAi line cross-validations also appeared within the screen itself. For example, in the primary screen using HHLT-GAL4, seven genes  were identified by two different RNAi lines, and two genes (Nup93-1 and Chc) by three different RNAi lines  identified several components of the COP9 Signalosome (CSN), an important negative regulator of cullin-RING ubiquitin ligases (CSN1b (identified twice), CSN2 , CSN5, and CSN6 (Nup88 (mbo), Nup93-1 (found twice), Nup214, and Nup358, genes encoding subunits (nucleoporins) of the Nuclear Pore Complex (NPC), which regulates nucleocytoplasmic shuttling, gene expression, and a variety of other cellular processes , Amphiphysin, three of four AP-2 adapter genes , Rab5, and Syntaxin7 suggests an important regulatory role for endosome formation and trafficking in hematopoiesis. The secondary screening, which examined the primary screen RNAi lines as well as additional RNAi lines targeting the same or related genes, identified CSN5 and CSN6 again  within this group . Not surprisingly, a large number of Gene Ontology (GO) terms, many of which are defined very broadly, were also found to be enriched within the Biological Process (456 GO terms), Molecular Function (40 GO terms), and Cellular Component (109 GO terms) categories (Supplementary Table S4). Comparison of our genes with the KEGG pathway database (q = 1.35e–07). KEGG analysis also identified the Spliceosome  and mRNA surveillance  groups, which, collectively, indicates an important role for RNA processing regulation during hematopoietic development. Gene set analysis by the Reactome pathway database (Supplementary Table S5), 11 of which coincide with RNA regulation (q = 1.1e–04), Phagosome , and SNARE interaction in vesicular transport , while 14 related pathways were identified by Reactome (Supplementary Table S5).To better understand the nature of the genes identified in the primary and the secondary screens, we analyzed each gene set using the online STRING protein interaction database  were found to be enriched among the secondary screening gene subsets  were also found to be enriched specifically among the secondary screen gene subsets. Notch signaling pathway gene enrichment shows up twice, identified by RNAi knockdown in PSC cells by Antp-GAL4, and in maturing blood cells by Δ-GAL4Hml  and Protein processing in endoplasmic reticulum (dome-GAL4), both enrichments were associated with a decrease in Δ-DsRedHml fluorescence. The Δ-DsRedHml phenotypes associated with the seven functional enrichment groups overlapping with the primary screen (HHLT-GAL4) can be found in The numbers of candidate hematopoietic genes identified by the secondary screening, using cell-type-specific RNAi along with the  subsets . Three amlΔ-GAL4 . FoxO siGAL4/UAS gene expression system, specifically the HHLT-GAL4 driver, the activity of which is highly restricted to the lymph gland and circulating blood cell populations (HHLT-GAL4 UAS-GFP). While indirect, this approach was advantageous because general lymph gland morphologies and circulating cell densities could be easily evaluated and compared across genotypes in situ, while also increasing the analytical throughput  that were either redundant or targeted genes that were functionally related to candidate hits from our primary screen. Second, we generated new GAL4 driver lines that (1) target RNAi to lymph gland sub-populations and (2) report on the development of mature blood cells. Our collection of 202 RNAi lines was then screened using these driver lines , and DsRed fluorescence was evaluated in whole animals, similar to GFP in the primary screen , and of how its functional disruption impacted lymph gland development. To achieve this, we first added additional RNAi lines  identified 34 genes regulating blood cell maturation and/or proliferation in the lymph gland, 19 causing an increase and 15 causing a decrease in Δ-DsRedHml expression  also identified a number of candidate genes regulating blood cell maturation. Specifically, progenitor-cell RNAi identified 50 genes, 33 that cause an increase and 17 that cause a decrease in Δ-DsRedHml fluorescence in the lymph gland. Previous work has shown lymph gland progenitor cells to be regulated by several paracrine and metabolic signaling mechanisms (Δ-GAL4Hml) identified the largest cell-type-specific subset with 56 candidate genes, 48 of which increase and 8 of which decrease Δ-DsRedHml fluorescence in the lymph gland. Previous work has demonstrated that interaction between mature cells and progenitor cells, via the “equilibrium signaling pathway,” is important for balancing progenitor cell maintenance and differentiation  by any single GAL4 driver in the secondary, cell type-specific screen. One possible reason is that HHLT-GAL4 phenotypes for most RNAi lines are complex, arising only because of functional disruption in multiple cell types simultaneously. Another possibility is a threshold effect owing to differences in GAL4 driver strength, i.e., the individual cell-type GAL4 drivers may not induce RNAi as robustly as HHLT-GAL4. For RNAi lines that do cause phenotypes both with HHLT-GAL4 and with a cell type-specific GAL4 driver, it is not clear that these are equivalent phenotypes. The absence of a hematopoietic marker in the HHLT-GAL4 screen and the differences in GAL4 expression levels and patterns contribute to this uncertainty. Thus, while we are confident that the candidate hematopoietic genes identified by HHLT-GAL4 in the primary phase of the screen are valid, it seems that determining the functional specificities of candidate genes may be more straightforward for those causing phenotypes when disrupted in a single hematopoietic cell type.It is interesting to note that many of the RNAi lines identified in the primary screen using P-value = 1.0e–16). Likewise, large numbers of Gene Ontology terms were also enriched for this network (Supplementary Table S4), though many of the terms are broad and overlapping. However, network analysis using the KEGG Pathway database identified a smaller number of enriched functional groups or pathways. Of the eight groups identified by KEGG analysis  and another three pointed to vesicular trafficking  as having major hematopoietic roles.Our analysis of the primary screen candidate genes using the online STRING database helped to reveal important genes subsets. The protein–protein interaction (PPI) network for our 137 gene dataset is composed of 599 edges (known or predicted interactions), a number significantly greater than the 350 edges expected for a randomly selected network of the same size , FoxO signaling pathway (dme04068), and Protein processing in endoplasmic reticulum (dme04141). It is interesting that Notch signaling pathway was identified twice by RNAi, once in the PSC (Antp-GAL4) and once in maturing cells (Δ-GAL4Hml), as both cell types have known roles for Notch signaling during hematopoiesis  is based upon identifying the genes chico, encoding the Insulin Receptor Substrate homolog, and babo, encoding the TGF-β/Activin receptor  can cause a variety of cellular defects including loss of apicobasal polarity, increased proliferation, and aberrant activation of signaling pathways such as Notch and EGFR , and a multifunctional chaperone called Hsc70-4. It is well established that functional disruption of early endosomal trafficking  and Prp19 are interesting because both encode components of the NineTeen Complex , as well as supernumary limbs  and Cullin 1   offered by the UCLA Minor in Biomedical Research. This RNAi-based screen for new hematopoietic genes represents the third iteration of a CURE-based pedagogical approach to teaching UCLA URCFG undergraduates about science and scientific research. The two previous research projects completed by the URCFG were mosaic analysis of lethal P-element insertional mutants in the fly eye  as the basis for the genetic screen was particularly beneficial. Using RNAi in the context of the To explore how students might benefit from participating in the RNAi screen, we used the SURE II survey , which an = 626). For URM students in particular, the increase in STEM retention is even greater . Our findings add to a growing body of evidence that authentic research experiences in the classroom context create highly effective learning environments for undergraduate students and can improve engagement and persistence in STEM  majors. It has been previously reported that the STEM retention rate nationally (through degree completion) is approximately 40%, dropping to as low as 25% among underrepresented minority (URM) students . As rece


Bcr-Abl and p210Bcr-Abl. While p210Bcr-Abl is the hallmark of chronic myeloid leukemia (CML), p190Bcr-Abl occurs in the majority of Philadelphia-positive acute lymphoblastic leukemia (Ph + ALL) patients. In CML, p190Bcr-Abl occurs in a minority of patients associating with distinct hematological features and inferior outcomes, yet the pathogenic role of p190Bcr-Abl and potential targeting therapies are largely uncharacterized. We employed next generation sequencing, phospho-proteomic profiling, and drug sensitivity testing to characterize p190Bcr-Abl in CML and hematopoietic progenitor cell line models (Ba/f3 and HPC-LSK). p190Bcr-Abl CML patients demonstrated poor response to imatinib and frequent mutations in epigenetic modifiers genes. In contrast with p210Bcr-Abl, p190Bcr-Abl exhibited specific transcriptional upregulation of interferon, interleukin-1 receptor, and P53 signaling pathways, associated with hyperphosphorylation of relevant signaling molecules including JAK1/STAT1 and PAK1 in addition to Src hyperphosphorylation. Comparable to p190Bcr-Abl CML patients, p190Bcr-Abl cell lines demonstrated similar transcriptional and phospho-signaling signatures. With the drug sensitivity screening we identified targeted drugs with specific activity in p190Bcr-Abl cell lines including IAP-, PAK1-, and Src inhibitors and glucocorticoids. Our results provide novel insights into the mechanisms underlying the distinct features of p190Bcr-Abl CML and promising therapeutic targets for this high-risk patient group.The oncogenic protein Bcr-Abl has two major isoforms, p190 BCR and ABL1 genes [BCR partner, different hybrid genes encoding distinct Bcr-Abl isoforms are formed [Bcr-Abl  and p210Bcr-Abl (referred to as p190 and p210 hereafter), based on apparent molecular weights [Bcr-Abl is a hybrid oncokinase protein, which is expressed upon t9;22) reciprocal translocation resulting in fusion between 2 recipro weights . Except  weights , 5. RAG  weights . The p19 weights –9.In CML, p190 occurs as the sole Bcr-Abl isoform at diagnosis in a minority of patients (1–2%) , 11 and Various models have been developed to investigate leukemogenic potential of different Bcr-Abl isoforms –18. In mIn order to explore the unique pathways involved in p190-CML leukemogenesis, we integrated exome sequencing, RNA sequencing and phosphorylation profiling to characterize a small cohort of p190-CML patients. We validated our findings by directly comparing the gene expression and phosphorylation profiles between p190 and p210 in two different hematopoietic progenitor cell lines (Ba/f3 and HPC-LSK). Furthermore, we used a comprehensive drug testing to investigate isoform-specific sensitivities and identified promising targeted therapies. Our study provides the first consolidated view of the molecular pathogenesis of p190-CML and identifies promising drug candidates which can be translated into tailored treatment strategies for these high-risk patients.n = 4), p210-CML patients (n = 3), and Ph+ALL patients (n = 10) are summarized in Supplementary Table Clinical and hematological data of p190-CML patients (−4 M 1-Thioglycerol (Sigma), 12.5 ng/ml recombinant human IL6 (Peprotech), 2 mM L-glutamin, and antibiotics as indicated for Ba/f3 cells  in addition to inhouse-prepared stem cell factor .Mouse Ba/f3 cell line retrovirally transfected with BCR-ABL1-GFP encoding either p190/p210 was a kind gift from Prof. Nikolas von Bubnoff . HPC-LSK progenitor cell lines  were genGenomic DNA was extracted from diagnostic PMNCs samples of four p190-CML patients. Processing of WES samples was performed as previously described  using HiTo screen the drug sensitivity profile of Ba/f3 cell lines, a comprehensive oncology drug library consisting of 156 approved drugs and 372 preclinical and investigational compounds was used. For the less proliferating HPC-LSK cells, a custom library of selected 65 compounds were designed and applied  featuring 228 phospho-tyrosine sites was used. Sample preparation and processing were performed according to manufacturer’ instructions. Array scanning and image analysis were performed by Full Moon Bioscience and data were normalized to the median antibody signaling value. Western blotting was used to validate findings in cell lines and CML patient samples (three p190 and three p210). For Ph+ALL samples, phosphorylation of selected proteins was investigated by flowcytometry using iQue Screener Plus flow cytometer (Intellicyt). Further details are described in Supplementary materials.3 cells were re-suspended in 0.4 ml IMDM culture medium supplemented by 2% FBS and the samples were mixed with 4 ml of methylcellulose  without adding cytokines. Drugs  and recombinant mouse IFN-alpha  were added in the indicated concentrations. 1.1 ml were plated on 35–mm-dishes in triplicate according to instructions of the manufacturer. Colonies were counted after 14 days using an inverted microscope. For phenotyping of the clones, cells were harvested from colonies at day 14 and stained with a panel of antimouse antibodies including CD19-PE , CD11b-PerCP-Cy™5.5 (#561114), CD45R/B220-BV421 (#562922) from BD Bioscience and CD3-APC (#100236), CD45-APC-Cy7 (# 557659) from Biolegend. Cells were acquired by FACSVerse (BD Biosciences).For HPC-LSK cell lines, 2.5 × 10t test, Fisher Exact test, Pearson’ correlation, and Spearman correlation tests were computed using GraphPad Prism 8 software or R 3.5.0.Mann–Whitney U-test, Two-tailed Student ASXL1, DNMT3A, and KDM4D epigenetic modifier genes   were identified as differentially expressed (DE) and were all upregulated in p190-CML compared to p210-CML samples. Among DE genes were CCL20, CXCL8 (IL8), IL1R2 , IFIT3, IFIT2 (type-I interferon-induced genes), GPR84 and S100P genes  showed notable hyperphosphorylation in p190-CML compared to p210-CML samples. Other STAT molecules including STAT3 and STAT5 showed mild hyperphosphorylation in P190-CML samples Fig. .Next, we aimed to get deeper insights of the p190-specific transcriptional and signaling pathways in hematopoietic progenitor cell lines to overcome the limitation of scarcity of p190-CML samples. We used two different models: Ba/f3 cell line, a mouse pro-B that has been extensively used in Bcr-Abl signaling comparative studies , 24 and Transcriptomic comparisons of the cell lines revealed a clear segregation between the parental and p190/p210-transduced cells in both models Fig. . DespiteQ < 0.05, Fig. Gbp), Stat1 and Stat2 signaling molecules, lymphocyte-related kinases and markers , and cytokine and chemokine molecules. In contrast, the overexpression of Cxcr4, Il15, Rab32, and Kalrn genes were demonstrated in p210 cells . Comparable to p190-CML, p190-Ph+ALL demonstrated increased Src, and to lesser extent STAT5, phosphorylation in contrast with STAT3 that was more phosphorylated in p210 samples  was downregulated in p190-CML (Supplementary Fig. GPR84 gene which is tightly linked to aberrant β-catenin activity [Jup was significantly upregulated in p190 HPC-LSK progenitor cell line model (Supplementary Fig. Transcriptomic regulation in p190 and p210 cells highlighted the differences in phenotypic potentials between the different Bcr-Abl isoforms. Earlier studies have shown that p190 and p210 induce distinct leukemia phenotypes in mouse models , 53, In Cxcr4  and Rab34 [Rab32  in p210 4 [Rab32 , that mi4 [Rab32 . In concactivity . In contIn summary, our data provides novel insights into the p190-specific genomic and signaling regulation in CML. Our data suggests a critical role of IFN/STAT1 signaling in p190-CML that represents a promising target of treatment. In addition, p190-CML patients typically showing inferior response to imatinib could benefit from frontline dasatinib treatment that additionally targets activated Src signaling. Drug sensitivity profiling also highlighted IAP-, PAK- and MDM2-inhibitors as potentially effective therapeutic options that can be combined with TKIs warranting their testing in personalized treatment strategies of p190-CML.SUPPLEMENTAL MATERIALSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Dataset 5


Tissue of origin, therefore, must be considered while assessing the effects of any particular heavy metal or metalloid. Among the metals and metalloid, arsenic appears to have relatively more pleiotropic influences on cellular metabolic pathways including those known to have association with diabetes. Although only two stem cell derived datasets are included in the current study, effects of heavy metals on these cells appear to be different from other mature cells of similar tissue origin. This study provides useful information about different heavy metal affected pathways, which may be useful in further exploration using wet-lab based techniques.Various anthropogenic and natural events over the years have gradually increased human exposure to various heavy metals. Several of these heavy metals including cadmium, mercury, nickel, chromium, and the metalloid arsenic among others, have created major public health concerns for their high level of toxicities. Identification of the general as well as the differentially affected cellular metabolic pathways will help understanding the molecular mechanism of different heavy metal-induced toxicities. In this study, we analyzed 25 paired (control vs. treated) transcriptomic datasets derived following treatment of various human cells with different heavy metals and metalloid  to identify the affected metabolic pathways. The effects of these metals on metabolic pathways depend not only on the metals  ArsenArsenic is the 20th most abundant element on earth’s crust  and wellCadmium (Cd) is another non-essential heavy metal that poses significant health risks to human. Chronic exposure to cadmium leads to systemic toxicity and cancers in the lung, breast, prostate, nasopharynx, pancreas, and kidney ,24. The Chromium in small amount is an essential element for normal metabolic functions. However, at high concentrations this heavy metal, especially in the hexavalent form (Cr (VI)), it is toxic and carcinogenic . Cr (VI)Other heavy metals like mercury, nickel, vanadium, etc also have adverse effects on human health. Human exposure to mercury occurs through inhalation of elemental  mercury vapor via industrial practices, dental amalgam or ingestion of organic mercury compounds  primarily through consumption of contaminated fish . Mercuryin silico tools to analyze 25 global gene expression datasets  of control vs. metal/ metalloid  treated cells of human origin to explore the metabolic pathways that are affected following exposure to arsenic and other heavy metals.In this study, we used 22.125 paired global gene expression datasets  from 13 studies in which different human cells  were exposed to arsenic, cadmium, chromium, mercury, nickel, iron, and vanadium were retrieved from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database  Table 1Table 1. 2.2The filtered list of genes from each paired dataset was used as an input into the NetworkAnalyst platform  and path33.1Irrespective of their tissue of origin, a particular heavy metal treated cell lines tend to cluster together A. The afArsenic exposed cells form a relatively distant cluster from the other heavy metal treated cells A. This mHg, Ni, Fe, and V affected pathways have many considerable overlaps and include purine metabolism, HIF-1A signaling pathway, fluid shear stress and atherosclerosis, protein digestion and absorption, glycerolipid metabolism, phosphatidylinositol signaling and ferroptosis pathways B). The dOne distinguishable pathway between Ni and the other  heavy metal treated cells is the pentose and glucuronate interconversion pathway, which appears to be under-represented in Ni treated cells. This particular pathway is under-represented in the Cr-treated cells as well. Except this one pathway, Cr appears to have the least impact on cellular metabolism. Effects of Fe, Hg, and V on purine metabolism and HIF-1A signaling pathways are more pronounced than those of Ni. In fact, Vanadate- a vanadium oxoanion, is known to induce expression of HIF-1α in a hypoxia-like manner, which in turn leads to progression and metastasis of cancer by increasing expression of vascular endothelial growth factor (VEGF) . Nickel As shown in The distinct cluster of the arsenic treated cells (except #8 and #9 in 3.2Datasets used in this study were derived from different cells of human origin. Two of these are human cancer cell lines- HepG2 (from liver cancer cells)  and MCF7Although different cell lines treated with same heavy metal/metalloid tend to cluster close to one another A, within3.3Impacts of arsenic on the cellular pathways in HepG2 cell line compared to the other cell lines are shown in Arsenic has drastically different effects on HepG2 cell line compared to other cell lines . All theThe PLS-DA plot along with the overrepresented pathways in cadmium treated cell lines are shown in Compared to arsenic, cadmium has less pronounced effect on metabolic pathways in HepG2 than the other cell lines . Among tCadmium is known to affect tight junctions, influenza A and Rap1 signaling pathways in the other cell lines. Influenza A virus up-regulates the expression of IL-27 in human lung epithelial cells and peripheral blood mononuclear cells through Cyclooxygenase-2 and Protein Kinase A (PKA) signaling . Rap1 pa3.4in vivo environment well despite its tumorigenic origin [c origin . The patc origin ,107. Addc origin . Hence, 4per se, but also the nature and origin of the cells. Therefore, the tissue origin must be considered while assessing the effects of a particular heavy metal/metalloid. Among the heavy metals and metalloid, arsenic appears to have more pleiotropic effects on cellular pathways, including a few known to have association with diabetes. One important limitation of this study is the inability to predict whether the affected pathways were induced or inhibited by the heavy metals. Despite this limitation, this study provides useful information about the pathways that are affected by particular heavy metals and may be directive in further exploration using wet-lab based techniques.This study explored the effects of several commonly encountered heavy metals and metalloid on the metabolic pathways of different cell lines of human origin. The affected metabolic pathways are modulated by not only the heavy metal This study neither involved any human nor animal, and hence no ethical approval was required.Ministry of Education, Bangladesh.Grant for Advanced Research in Education (GARE) from the Design of the work- AAS; Acquisition, analysis, and interpretation of data- KF, SSS, TA, ZH, AFS; Manuscript preparation and reviewing- SSS, KF, TA, AASThe authors declare no conflict of interest.


Colorectal cancer is a global issue with millions of patients in need for new treatment options. Surgical resection is often the first clinical intervention and can be, especially for primary tumours, curative. However, advanced tumours, recurrences, and metastases require drug treatment. Our aim was to compare material of primary tumours and corresponding metastases to draw conclusions from the first one to the latter. The feasibility of therapy prediction using the primary tumour material for the metastatic situation creates a time window for functional testing, which can realistically be integrated into the strict timeframe of clinical procedures. A reliable treatment recommendation, based on the tumour drug response, would improve treatment success in patients with metachronous metastases and spare them from unnecessary side effects of unsuccessful therapies.The prognosis of metastatic colorectal cancer (CRC) remains poor. Patients and physicians are in need of individual therapies and precise response predictions. We investigated the predictive capacity of primary tumour material for treatment response of metastases. Mutational landscapes of primary tumours and corresponding metastases of 10 CRC patients were compared. Cell line characteristics and chemosensitivity were investigated pairwise for primary and metastatic tumours of four patients. PDX models of one patient were treated in vivo for proof of concept. Driver mutations did not differ between primaries and metastases, while the latter accumulated additional mutations. In vitro chemosensitivity testing revealed no differences for responses to 5-FU and oxaliplatin between primary and metastatic cell lines. However, irinotecan response differed significantly: the majority of metastases-derived cell lines was less sensitive to irinotecan than their matching primary counterpart. Therapy recommendations based on these findings were compared to clinical treatment response and mostly in line with the predicted outcome. Therefore, primary tumour cell models seem to be a good tool for drug response testing and conclusion drawing for later metastases. With further data from tumour-derived cell models, such predictions could improve clinical treatment decisions, both recommending likely effective therapeutic options while excluding ineffective treatments. Colorectal cancer (CRC) is one of the most commonly diagnosed cancers, with 1.4 million new cases annually and almost 700,000 deaths worldwide . TreatmeThese agents can be applied in a neoadjuvant or adjuvant setting and even in cases of advanced tumour progression or in the metastatic situation. A common first-line chemotherapeutic agent for CRC is fluorouracil 5-FU) which is often applied in combination with oxaliplatin and/or irinotecan to increase response rates . Even th-FU whichTo further this individualised therapeutic approach, we established patient-derived cancer models from primary tumours as well as matched metastases from CRC patients with different tumour locations, molecular subtypes and mutational profiles. Either patient tumour material or patient-derived xenograft tissues were used to start the cell line establishment process .We could successfully establish cell line pairs of primary carcinomas and their corresponding metastases from seven different CRC patients. In one of those cases, we were even able to generate a cell line out of the primary tumour, a synchronous as well as a metachronous metastasis. Four of these cell line pairs were analysed in detail with regard to growth behaviour, molecular pattern and chemosensitivity. We studied the response of these matching cell lines side-by-side to the first-line chemotherapeutics 5-FU, oxaliplatin and irinotecan in vitro. For the set consisting of primary, synchronous and metachronous model, we additionally analysed drug responses in PDX models in vivo.The aim of the study was to unveil commonalities and differences between primaries and metastases. And more importantly, we explored the feasibility of drawing conclusions from primary tissue characteristics  to metastasis. This option would pave the way for predicting treatment success for metastases using the primary material and thus save precious time in the clinical context.Cell lines were established as described in detail before . In shorNames of the cell lines consist of the following information: Place of material collection (HRO = Hanse City of Rostock), tumour entity (C = Colon), patient number. In the case of a collected metastasis, “Met” is added after the patients’ ID, and in the case of xenograft-derived cell lines number of tumour transfer in mice  and mouse identifier  are included in the cell line name.Foxn1nu mice  weighing 18–22 g. Mice received standard pellet food and water ad libitum. Tumour pieces from previously xenografted patients’ tumours with less than 5 mouse transfers were used and implanted subcutaneously into the animals’ right flank under anaesthesia . Tumour growth was regularly monitored, and therapy was initiated upon tumour establishment (ca. 65 mm3). Tumour size was measured using a slide scale, and tumour volume was calculated with the following formula:PDX were established as described in detail before . Under s3) were randomised into four treatment groups: (1) 5-FU (30 mg/kg bw) (2) irinotecan (20 mg/kg bw) (3) FIRI (5-FU 30 mg/kg bw and irinotecan 20 mg/kg bw) (4) control group receiving equal amount of 0.9% NaCl solution. All therapeutic regimens were applied i.p. thrice weekly for a total of 6 injections. Tumour growth was monitored thrice weekly, within therapy as well as during one week of follow up time. Termination criteria were weight loss >20% vs. start of therapy, impaired socio-physiological behaviour, or tumour sizes >1500 mm3. For detailed examinations, tumour tissues were resected, dissected, and cryopreserved as well as embedded in paraffin. H&E sections (4 µm) were prepared for histopathological examinations, according to standard protocols for surgical pathology reports. In a blinded study, morphology of tumour architecture, cytological features, and signs of therapy-induced necrosis/apoptosis of all obtained PDX material was examined and scored  by an expert pathologist (F.P.).Mice with solid tumours  were seeded in triplicates in 150 µL/well standard medium in a flat 96-well plate and allowed to attach overnight. Then, chemotherapeutics were added in 50 µL medium yielding the following final concentration ranges: (1) 1 nM–5000 µM 5-FU; (2) 0.1 nM–1000 µM irinotecan; (3) 1.3 nM–20 µM oxaliplatin and (4) FIRI. Medium exchange was performed after 72 h with the addition of fresh chemotherapeutics. After a second 72 h treatment period, viability of the cells was measured.Wells were washed with PBS to remove dead cells and fragments before the staining. For 96-well plates, 50 µL/well crystal violet staining solution was used. After 20 min incubation at room temperature, wells were washed three times with PBS and dried. To solubilize the colorant, 100 µL of 1% sodium dodecyl sulfate solution was added and incubated for 10 min on a plate shaker. Measurement of absorbance at 590 nm was performed using a Tecan Infinite 200 Pro plate reader and percentage of living cells was determined as follows:IC50 values were calculated using GraphPad Prism 5 and heatmaps were created using TBtool .After the preparation of a single cell suspension 100 cells/well were seeded in quadruplets in a 96-well plate in 200 µL standard medium. Outgrowing colonies were stained after 14–20 days with crystal violet as described above and counted manually.After the preparation of a single cell suspension, 100,000 cells/well were seeded in a 6-well plate for suspension cell culture in 5 mL defined serum free medium. Spheroid formation was evaluated qualitatively after 7–14 days and photographically documented.https://www.ncbi.nlm.nih.gov/clinvar/, accessed on 28 August 2021) followed by a manual evaluation.Tumour DNA was isolated from PDX samples snap frozen in liquid nitrogen by using the Precellys Tissue DNA Kit . Quality criteria for DNA included a concentration ≥25 ng/µL and a DNA integrity number ≥5. Next Generation Sequencing was than performed by Centogene  applying the augmented Solid Tumour Panel including 105 fully sequenced genes plus mutational hot spots from additional 146 genes see . A coverConcerning the NGS data, for this study, we focused on mutations classified as either pathogenic or likely pathogenic as well as mutations of uncertain significance. Excluded were all synonymous and benign as well as likely benign mutations. Quality criteria included a variant allele frequency of at least 25.We collected tumour and corresponding metastatic material of 10 patients  aged 44–82 years . Detailed patient characteristics and clinical information can be depicted from TNM classification of the primary tumours revealed medium and high-grade adenocarcinomas  classified to UICC III (3 patients) or IV (7 patients). The majority of metastases were found in the liver (9), but three patients developed metastases either in the peritoneum (2) or in the brain (1) see . Six patMost tumours and metastases were untreated when resected. Only one patient (HROC348) received neo-adjuvant irradiation and only HROC277Met2 was drug-treated before metastatic resection see . FollowiThe ten analysed patient tumours’ molecular subtype could be classified as sporadic standard CRC (6/10), CpG island methylator phenotype high and microsatellite stable CRC (3/10) as well as sporadic microsatellite instable CRC (1/10) .The analysis of genetic aberrations in tumours and metastases revealed a relatively low number of mutations detected in the selected panel. This included 251 tumour relevant genes. After application of quality and exclusion criteria we detected 30 genetic aberrations  including well-known driver mutations like G12D in the oncogene KRAS and truncating mutations of the tumour suppressor gene APC. Furthermore, there were two genes  mutated exclusively in tumours and eight  solely in metastases. Especially in the metastases, there was an increase of mutations classified as ‘uncertain significance’, which represent more than 40% of all detected alterations.In four of the ten cases, the number of mutations did not vary between tumour and metastasis, whereas six pairs presented with more mutations in the metastasis. The tumour and Met2 of HROC103 showed one mutation while the Met1 had an additional mutation. There were commonalities as well as differences between tumours and metastases regarding the mutated genes. Comparing their mutational profile, three sets of tumour and metastases were similar, whereas the detected mutations of seven patients differed between tumour and metastasis see . We founAPC, KRAS and TP53. Mutations in the APC gene were detected in the samples of four patients, which showed all individual mutations  pathogenic mutations were detected in BRAF, HNF1A, SMAD4, MUTYH, FBXW7 and TSC2. Pathogenic and likely pathogenic mutations were mainly consistent in tumours and metastases. Interestingly, HROC103 showed only mutations of uncertain significance and no (likely) pathogenic mutation was detected.Focusing on (likely) pathogenic mutations, we identified 33 mutations in nine genes (0–3 mutations per sample). The most frequently mutated genes were ions see . NotablyFor in vitro experiments, cell line pairs of the following patients were selected based on feasibility criteria  and further characterised: HROC147, HROC277, HROC278, and HROC348. For an overview, see HROC147 T0 M1 was established from a xenograft, whereas the cell line of the corresponding metastasis HROC147Met1 originates directly from patient material. Cells of both cell lines grew in dense, clearly outlined round colonies limited in size and did not form a confluent monolayer. Doubling times of HROC147 T0 M1 and HROC147Met1 were 63 h and 58 h, respectively.The cell lines of patient HROC277 differed in their morphology as well as their proliferation rate. HROC277 T0 M1 and HROC277Met2 appeared to grow in colonies without contact inhibition, leading to the establishment of a confluent monolayer. On the other hand, HROC277Met1 T0 M2 favoured growth in single colonies not spreading over the culture vessel. The lowest proliferation rate in this set of cell lines was observed for HROC277Met1 T0 M2 with a doubling time of 66 h. The doubling times of HROC277 T0 M1 and HROC277Met1 T0 M2 were similar with 62 h.The xenograft-derived matching cell lines of patient HROC278 were dissimilar in their cultivation appearance. The cell line of the tumour, HROC278 T0 M1, formed small and dense colonies with miniature adjacent colonies. HROC278Met1 T2 M2 grew in bigger and less dense colonies. According to their morphology, their rate of proliferation differed, too. The cell line of the metastasis, HROC278Met1 T2 M2, grew slightly faster than the tumour-derived cell line (54 h versus 61 h).HROC348 and the corresponding metastasis HROC348Met1 were directly patient-derived cell lines. They both favoured growth on collagen-coated cell culture vessels and built similar irregularly shaped colonies that were characterised by small cells. Doubling times of HROC348 and HROC348Met1 were 74 h and 66 h, respectively.p < 0.001). The opposite was observed in the cell line pair of HROC348. Here, the metastasis-derived cell line formed significantly more colonies than the tumour (p < 0.001).All cell lines used were tested positive in the colony formation assay, thereby proving their capacity to form colonies out of single cells see . A signiIn the spheroid formation assay, most tested cell line pairs grew into spheroids with no morphological differences between tumour-derived and metastasis-derived cell lines. Only the spheroids of HROC278 T0 M2 and HROC278Met1 T2 M2 did not resemble each other. HROC278 T0 M2 formed spheroids of irregular shape with dark inclusions, whereas spheroids of HROC278Met1 T2 M2 were round and of light colour.50 values ranged from 0.5 to 9.2 µM . The results for the cell lines from case HROC277 were even more diverse. HROC277Met2 was least responsive , followed by HROC277Met1 T0 M2  and HROC277 T0 M1, which was relatively sensitive (IC50[HROC277 T0 M1] = 0.1 µM).A remarkable difference was observed for the responses to irinotecan see B. HROC1450[HROC348] = 2.1 µM; IC50 = 16.8 µM), the IC50 exceeded the normal plasma level of irinotecan (up to 2 µM [Our in vitro chemosensitivity testing showed the most diverse results for irinotecan. All tested CRC cell lines reacted to the treatment and no intrinsic resistance was observed (acquired resistance under long term treatment was not tested). But the metastasis-derived cell lines of three patients were less sensitive towards irinotecan than the tumour cell lines. This is in accordance with results from Takebayashi et al. who also observed lower sensitivity, especially in liver metastatic cells compared to paired CRC primary tumour cells when treating with irinotecan, but not with 5-FU or oxaliplatin . In the  to 2 µM ) by far;We further tested if combinations of the chemotherapeutics 5-FU and irinotecan act synergistically in lower doses. Contrarily to what was expected, viability decreasing effects of irinotecan were abrogated by 5-FU.Exemplarily, we selected an in vivo PDX model set of one CRC patient from whom we had PDX models of the primary tumour (HROC277), the synchronous (HROC277Met1) and the metachronous (HROC277Met2) metastases for validation of these in vitro results. All PDX models were treated with 5-FU, irinotecan and their combination FIRI. The results confirmed that a combinational approach with these chemotherapeutic substances is not superior to the best-acting single chemotherapy, i.e., irinotecan; and thus corroborating the in vitro results of HROC277. In contrast, the different responsive behaviours of the metastasis-derived cell clones compared to the primary-derived ones under irinotecan treatment obtained in vitro could not be reproduced. One explanation for this phenomenon could be the comparatively low activity of irinotecan compared to its hydrolysed compound SN-38 . MetabolBased on these in vitro and in vivo results, we retrospectively stated therapy recommendations and compared them with the actual clinical treatment success of the patients. For patient HROC147, we would have proposed chemotherapy including 5-FU and oxaliplatin. Indeed, FOLFOX  therapy led to a complete remission of the patient.50 values for irinotecan. The promising results from the in vivo experiments probably trace back to the much higher plasma level in the mouse [50 value of all tested cell lines for irinotecan. With this knowledge, the patient could have been spared from irinotecan-containing treatments including their side effects and other promising therapy options should have been used in first-line treatment. Even the primary tumour presented a rather high IC50 for irinotecan in the range of reachable plasma levels, indicating potential treatment difficulties.Patient HROC277 underwent surgical resection of the tumour and its synchronous metastasis before chemotherapy started with FOLFOX, followed by FOLFIRI. The metachronous metastasis HROC277Met2, detected under FOLFIRI treatment, was resected subsequently. But the following FOLFIRI cycles did not result in tumour control and the disease progressed with further metastases. Our in vitro studies match these potential treatment failures with relatively high IChe mouse  comparedThe cell lines of HROC278 were highly sensitive for 5-FU and irinotecan and intermediate for oxaliplatin proposing an excellent treatment success. Nevertheless, FOLFIRI plus bevacizumab therapy after resection failed to prevent the development of a further metastasis, and the patient died 26 months later. As a likely explanation, the relatively high tumour burden due to the incomplete resection may have caused rapid disease progression by proliferation of chemo-resistant subclones.The initial question of this study was whether it is possible to predict biological behaviour of metastases by concluding results obtained from CRC cells of the primary tumour. As an ideal tool to answer this question, the cell line pairwise comparison confirmed that both phenotype and growth behaviour of CRC cells did not differ significantly between tumour- and metastasis-derived cells. Also, the chemosensitivity tests revealed no significant differences between tumour and metastasis for the majority of cell line pairs and tested substances. Consequently, we would conclude that analysing and testing cells from primary CRC allows for identifying promising treatments for metastases likely to develop in the future. Besides approaching chemo response with 2D cell cultures, patient-derived organoids (PDOs) delivered also encouraging results. Studies investigating PDOs from either tumour or metastatic material could confirm a correlation between PDO reactivity and patient’s response to chemotherapy ,46. Howe