Name: characterizing mutational load and clonal composition of human blood
Uploaded: 2019-07-11T15:00:00.000+00:00
Duration: 7 min 58 s
Description: Watch this Scientific Journal Video about Characterizing Mutational Load and Clonal Composition of Human Blood at JoVE.com

&#65279;This study was supported by a &#65279;a VIDI grant of the Netherlands Organization for Scientific Research (NWO) (no. 016.Vidi.171.023) to R. v. B.

<ol>
	<li>Osorio, F. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Somatic+Mutations+Reveal+Lineage+Relationships+and+Age-Related+Mutagenesis+in+Human+Hematopoiesis.">Somatic Mutations Reveal Lineage Relationships and Age-Related Mutagenesis in Human Hematopoiesis.</a> Cell Reports. 25, 2308-2316 (2018).</li><li>Genovese, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clonal+Hematopoiesis+and+Blood-Cancer+Risk+Inferred+from+Blood+DNA+Sequence.">Clonal Hematopoiesis and Blood-Cancer Risk Inferred from Blood DNA Sequence.</a> New England Journal of Medicine. 371, 2477-2487 (2014).</li><li>Jaiswal, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Age-Related+Clonal+Hematopoiesis+Associated+with+Adverse+Outcomes.">Age-Related Clonal Hematopoiesis Associated with Adverse Outcomes.</a> New England Journal of Medicine. 371, 2488-2498 (2014).</li><li>Young, A. L., Challen, G. A., Birmann, B. M., Druley, T. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clonal+haematopoiesis+harbouring+AML-associated+mutations+is+ubiquitous+in+healthy+adults.">Clonal haematopoiesis harbouring AML-associated mutations is ubiquitous in healthy adults.</a> Nature Communications. 7, 1-7 (2016).</li><li>Alexandrov, L. B., Nik-Zainal, S., Wedge, D. C., Campbell, P. J., Stratton, M. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deciphering+Signatures+of+Mutational+Processes+Operative+in+Human+Cancer.">Deciphering Signatures of Mutational Processes Operative in Human Cancer.</a> Cell Reports. 3, 246-259 (2013).</li><li>Alexandrov, L. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Signatures+of+mutational+processes+in+human+cancer.">Signatures of mutational processes in human cancer.</a> Nature. 500, 415-421 (2013).</li><li>Alexandrov, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Repertoire+of+Mutational+Signatures+in+Human+Cancer.">The Repertoire of Mutational Signatures in Human Cancer.</a> bioRxiv. , (2018).</li><li>Behjati, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome+sequencing+of+normal+cells+reveals+developmental+lineages+and+mutational+processes.">Genome sequencing of normal cells reveals developmental lineages and mutational processes.</a> Nature. 513, 422-425 (2014).</li><li>Blokzijl, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tissue-specific+mutation+accumulation+in+human+adult+stem+cells+during+life.">Tissue-specific mutation accumulation in human adult stem cells during life.</a> Nature. 538, 260-264 (2016).</li><li>Gawad, C., Koh, W., Quake, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+genome+sequencing:+Current+state+of+the+science.">Single-cell genome sequencing: Current state of the science.</a> Nature Reviews Genetics. 17, 175-188 (2016).</li><li>Dong, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accurate+identification+of+single-nucleotide+variants+in+whole-genome-amplified+single+cells.">Accurate identification of single-nucleotide variants in whole-genome-amplified single cells.</a> Nature Methods. 14, 491-493 (2017).</li><li>Welch, J. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+origin+and+evolution+of+mutations+in+acute+myeloid+leukemia.">The origin and evolution of mutations in acute myeloid leukemia.</a> Cell. 150, (2012).</li><li>Jager, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+mutation+accumulation+in+single+human+adult+stem+cells+by+whole-genome+sequencing+of+organoid+cultures.">Measuring mutation accumulation in single human adult stem cells by whole-genome sequencing of organoid cultures.</a> Nature Protocols. 13, 59-78 (2018).</li><li>Boeva, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Control-FREEC:+A+tool+for+assessing+copy+number+and+allelic+content+using+next-generation+sequencing+data.">Control-FREEC: A tool for assessing copy number and allelic content using next-generation sequencing data.</a> Bioinformatics. 28, 423-425 (2012).</li><li>Blokzijl, F., Janssen, R., van Boxtel, R., Cuppen, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MutationalPatterns:+Comprehensive+genome-wide+analysis+of+mutational+processes.">MutationalPatterns: Comprehensive genome-wide analysis of mutational processes.</a> Genome Medicine. 10, 1-11 (2018).</li><li>Thorvaldsd&#243;ttir, H., Robinson, J. T., Mesirov, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrative+Genomics+Viewer+(IGV):+High-performance+genomics+data+visualization+and+exploration.">Integrative Genomics Viewer (IGV): High-performance genomics data visualization and exploration.</a> Briefings in Bioinformatics. 14, 178-192 (2013).</li><li>Notta, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Isolation+of+single+human+hematopoietic+stem+cells+capable+of+long-term+multilineage+engraftment.">Isolation of single human hematopoietic stem cells capable of long-term multilineage engraftment.</a> Science. , (2011).</li><li>Lee-Six, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Population+dynamics+of+normal+human+blood+inferred+from+somatic+mutations.">Population dynamics of normal human blood inferred from somatic mutations.</a> Nature. 561, 473-478 (2018).</li><li>Behjati, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome+sequencing+of+normal+cells+reveals+developmental+lineages+and+mutational+processes.">Genome sequencing of normal cells reveals developmental lineages and mutational processes.</a> Nature. , (2014).</li></ol>

The authors have nothing to disclose.

Presented here is a method to detect mutations that accumulated during life in individual HSPCs and to construct an early developmental lineage tree using these mutation data.
Several critical requirements must be met in order to successfully perform these assays. First, the viability of the sample must be ensured. Quick handling of the sample is key to ensure the efficiency of the procedure. Second, loss of growth factor potency will negatively affect the clonal expansion of HSPCs. To ensure high growth factor potency, it is important to avoid freeze-thaw cycles and prepare single-use aliquots. Third, after performing WGS, mutation calling and filtering, it is crucial to validate the clonality of the clonal culture. To confirm the clonality of the culture, the VAF of the mutations should cluster around of 0.5 in a karyotypically normal sample (Figure 3). In cells with a low mutational load, such as cord blood HSPCs, it is more difficult to determine clonality due to the low mutation numbers.
Our approach relies on in vitro expansion of single cells to allow for WGS. Therefore, our approach is restricted to cells that have the replicative potential to clonally expand, such as HSPCs. In our hands, about 5%-30% of all single-sorted cells are able to expand adequately. Reduced outgrowth rates can potentially result in a selection bias. As discussed previously, methods using WGA can overcome this selection bias as this technique is does not rely on the expansion of cells. However, WGA has its own shortcomings, and clonal amplification remains the only method to accurately determine the number of mutations in the whole genome without allelic dropouts and equal coverage along the genome, especially in samples with low true somatic mutation numbers.
The data generated using this approach can be used to determine phylogenies of the hematopoietic system, as the mutations detected in single cells can be used to dissect cell lineages, as depicted in Figure 6. Typically, one or two mutations can define each branch in a healthy donor1. Since lineages branch early after conception, mutations defining these first branches will also be present with a low VAF in the matched normal sample that was used for filtering the germline variants1,18,19. In this case, the use of non-hematopoietic cells, such as MSCs, are preferred as they are expected to separate very early during development from the hematopoietic system. As T-cells are of hematopoietic origin, the use of these cells as a matched normal sample to filter germline variants could therefore confound the construction of the earliest branching of the developmental lineage tree. Subclonal presence of branch-specific mutations in certain mature blood populations, which can be measured by targeted deep sequencing, will indicate that the progeny of that branch can give rise to that mature cell type. In addition, our approach allows for assessing the mutational consequences of mutagenic exposure in vivo and ultimately how this may contribute to leukemia development.

Exposure of hematopoietic stem and progenitor cells (HSPCs) to endogenous or extrinsic mutagenic sources contributes to the gradual accumulation of mutations in the DNA during a lifespan1. Gradual mutation accumulation in HSPCs1 can result in age-related clonal hematopoiesis (ARCH)2,3, which is a non-symptomatic condition driven by HSPCs carrying leukemia-driver mutations. Initially, it was thought that individuals with ARCH have an increased risk for leukemia2,3. However, recent studies have shown an incidence of 95% of ARCH in elderly individuals4, making the association with malignancies less clear and raising the question of why some individuals with ARCH eventually do or do not develop malignancies. Nonetheless, somatic mutations in HSPCs can pose serious health risks, as myelodysplastic disorders and leukemia are characterized by the presence of specific cancer driver mutations.
To identify the mutational processes and study blood clonality, mutation accumulation in individual HSPCs needs to be characterized. Mutational processes leave characteristic patterns in the genome, so-called mutational signatures, which can be identified and quantified in genome-wide collections of mutations5. For instance, exposure to UV light, alkylating agents, and defects in DNA repair pathways have each been associated with a different mutational signature6,7. In addition, due to the stochastic nature of mutation accumulations, most (if not all) of the acquired mutations are unique between cells. If mutations are shared between multiple cells of the same individual, it indicates that these cells share a common ancestor8. Therefore, by assessing shared mutations, lineage relationships can be determined between cells and a developmental lineage tree can be constructed branch by branch. However, cataloguing rare somatic mutations in physiologically normal cells is technically challenging due to the polyclonal nature of healthy tissues.
Presented here is a method to accurately identify and determine somatic mutations in the genomes of individual HSPCs. This involves the isolation and clonal expansion of HSPCs in vitro. These clonal cultures reflect the genetic makeup of the original cell (i.e., mutations in the original cell will be shared by all other cells in the culture). This approach allows us to obtain sufficient DNA for whole genome sequencing (WGS). We have previously shown that mutations accumulated in vitro during clonal culture will be shared by a subset of cells. This enables the filtering of all in vitro mutations, as these will be present in a smaller fraction of reads compared to in vivo acquired mutations9. Previous methods have obtained sufficient DNA from a single cell for WGS using whole-genome amplification (WGA)10. However, the main disadvantage of WGA is its relatively error-prone and unbalanced amplification of the genome, which can result in allele dropouts11. Nonetheless, as this approach relies on in vitro expansion of single cells, it is limited to blood cells with sufficient replicative potential, which is not the case for WGA-dependent methods. Earlier efforts sequencing clonal cultures have relied on using feeder layers to ensure clonal amplification of single HSPCs12. However, DNA from the feeder layers can potentially contaminate the DNA of the clonal cultures, confounding the subsequent mutation calling and filtering. The method presented here solely relies on specified medium to clonally expand single HSPCs, and therefore avoids the issue of DNA contamination. Until now, we have successfully applied this method on human bone marrow, cord blood, viably frozen bone marrow, and peripheral blood.

<table><tbody><tr><td>0.20 &amp;micro;m syringe filter</td><td>Corning</td><td>431219</td><td></td></tr><tr><td>50 mL Syringe, Luer lock</td><td>BD</td><td>613-3925</td><td></td></tr><tr><td>Bovine Serum Albumin (BSA)</td><td>Sigma-Aldrich</td><td>A9647-50G</td><td></td></tr><tr><td>CD11c FITC</td><td>BioLegend</td><td>301603</td><td>Clone 3.9</td></tr><tr><td>CD16 FITC</td><td>BioLegend</td><td>302005</td><td>Clone 3G8</td></tr><tr><td>CD3 BV650</td><td>Biolegend</td><td>300467</td><td>Clone UCHT1</td></tr><tr><td>CD34 BV421</td><td>BioLegend</td><td>343609</td><td>561</td></tr><tr><td>CD38 PE</td><td>BioLegend</td><td>303505</td><td>Clone HIT2</td></tr><tr><td>CD45RA PerCP/Cy5.5</td><td>BioLegend</td><td>304121</td><td>Clone HI100</td></tr><tr><td>CD49f PE/Cy7</td><td>BioLegend</td><td>313621</td><td>Clone GoH3</td></tr><tr><td>CD90 APC</td><td>BioLegend</td><td>328113</td><td>Clone 5E10</td></tr><tr><td>Cell Strainer 5 mL tube</td><td>Corning</td><td>352235</td><td></td></tr><tr><td>CELLSTAR plate, 384w, 130 &amp;micro;L, F-bottom, TC, cover</td><td>Greiner</td><td>781182</td><td></td></tr><tr><td>Cryogenic vial</td><td>Corning</td><td>430487</td><td></td></tr><tr><td>Dimethyl sulfoxide (DMSO)</td><td>Sigma-Aldrich</td><td>D2650</td><td></td></tr><tr><td>DMEM/F12</td><td>ThermoFisher</td><td>61965059</td><td></td></tr><tr><td>EDTA</td><td>Sigma-Aldrich</td><td>E4884-500G</td><td></td></tr><tr><td>Fetal Bovine Serum</td><td>ThermoFisher</td><td>10500</td><td></td></tr><tr><td>GlutaMAX</td><td>ThermoFisher</td><td>25030081</td><td></td></tr><tr><td>Human Flt3-Ligand, premium grade</td><td>Miltenyi Biotech</td><td>130-096-479</td><td>Reconsititute in single-use aliquots (25 &amp;mu;L) at 100 &amp;mu;g/mL in 0.1% BSA in PBS</td></tr><tr><td>Human Recombinant IL-3 (E. coli-expressed)</td><td>Stem Cell Technologies</td><td>78040.1</td><td>Reconsititute in single-use aliquots (2.5 &amp;mu;L) at 100 &amp;mu;g/mL in 0.1% BSA in PBS</td></tr><tr><td>Human Recombinant IL-6 (E. coli-expressed)</td><td>Stem Cell Technologies</td><td>78050.1</td><td>Reconsititute in single-use aliquots (5 &amp;mu;L) at 100 &amp;mu;g/mL in 0.1% BSA in PBS</td></tr><tr><td>Human SCF, premium grade</td><td>Miltenyi Biotech</td><td>130-096-695</td><td>Reconsititute in single-use aliquots (25 &amp;mu;L) at 100 &amp;mu;g/mL in 0.1% BSA in PBS</td></tr><tr><td>Human TPO, premium grade</td><td>Miltenyi Biotech</td><td>130-095-752</td><td>Reconsititute in single-use aliquots (12.5 &amp;mu;L) at 100 &amp;mu;g/mL in 0.1% BSA in PBS</td></tr><tr><td>Integrative Genomics Viewer 2.4</td><td>Broad Institute</td><td></td><td>https://software.broadinstitute.org/software/igv/download</td></tr><tr><td>Iscove&#039;s Modified Eagle&#039;s Medium</td><td>ThermoFisher</td><td>12440061</td><td></td></tr><tr><td>Lineage (CD3/14/19/20/56) FITC</td><td>BioLegend</td><td>348701</td><td>Clones: UCHT1, HCD14, HIB19, 2H7, HCD56</td></tr><tr><td>Lymphoprep</td><td>Stem Cell Technologies</td><td>#07861</td><td>Used for Density gradient separation</td></tr><tr><td>PBS</td><td></td><td></td><td>Made in at Institute&#039;s facility. Commerically available PBS can also be used</td></tr><tr><td>Penicillin-Streptomycin</td><td>ThermoFisher</td><td>15140122</td><td></td></tr><tr><td>Primocin</td><td>Invivogen</td><td>ant-pm-1</td><td>Antibiotic formulation</td></tr><tr><td>QIAamp DNA Micro Kit</td><td>Qiagen</td><td>56304</td><td></td></tr><tr><td>Qubit 2.0 fluorometer</td><td>ThermoFisher</td><td>Q32866</td><td></td></tr><tr><td>Qubit dsDNA HS Assay Kit</td><td>ThermoFisher</td><td>Q32854</td><td></td></tr><tr><td>RNAse A</td><td>Qiagen</td><td>19101</td><td></td></tr><tr><td>SH800S Cell Sorter</td><td>Sony</td><td>SH800S</td><td></td></tr><tr><td>StemSpan SFEM, 500 mL</td><td>Stem Cell Technologies</td><td>9650</td><td></td></tr><tr><td>TE BUFFER PH 8.0, LOW EDTA</td><td>G-Biosciences</td><td>786-151</td><td></td></tr><tr><td>TrypLE Express</td><td>ThermoFisher</td><td>12605-10</td><td></td></tr></tbody></table>

characterizing mutational load and clonal composition of human blood

Hematopoietic stem and progenitor cells (HSPCs) gradually accumulate DNA mutations during a lifespan, which can contribute to age-associated diseases such as leukemia. Characterizing mutation accumulation can improve understanding of the etiology of age-associated diseases. Presented here is a method to catalogue somatic mutations in individual HSPCs, which is based on whole-genome sequencing (WGS) of clonal primary cell cultures. Mutations that are present in the original cell are shared by all cells in the clonal culture, whereas mutations acquired in vitro after cell sorting are present in a subset of cells. Therefore, this method allows for accurate detection of somatic mutations present in the genomes of individual HSPCs, which accumulate during life. These catalogues of somatic mutations can provide valuable insights into mutational processes active in the hematopoietic tissue and how these processes contribute to leukemogenesis. In addition, by assessing somatic mutations that are shared between multiple HSPCs of the same individual, clonal lineage relationships and population dynamics of blood populations can be determined. As this approach relies on in vitro expansion of single cells, the method is limited to hematopoietic cells with sufficient replicative potential.

Experimental procedure 
The experimental workflow is depicted in Figure 1. Based on the type of input material, different steps must be followed. In Figure 2 a flow cytometric output of a cord blood cell sort is depicted. First, all monocytic cells are selected by loosely drawing a gate around this population. Then, singlets are isolated by selecting for cells with a linear FSC-H/FSC-A ratio, as a lower FSC-H/FSC-A ratio includes doublets or cell clumps. The unstained control sample is used to define cell sorting gates for lineage-, CD34+, CD38-, CD45RA-. Additionally, CD90 and CD49f can be used to distinguish between progenitor cells or self-renewing stem cells17 (Figure 2). Index sorting enables the re-tracing of individual cells, and the sorted cells are depicted as brown dots. During cell culture, individual clones can expand at a different pace, with some clones expanding within 3 weeks, while other clones are only fully expanded until the fifth week of culture. See Figure 3A,B for representative colony outgrowth. A representative picture is shown of a nearly confluent MSC bulk culture at 11 days after plating (Figure 3C).
Checking quality after sequencing and mutation analysis 
Shown is an example output of the copy number analysis generated by Control-FreeC14 to check for copy number alterations (Figure 4). Karyotypic information can indicate which chromosomes to exclude during a SNVFI run (step 7.6). The VAF plot created by SNVFI (Figure 5) is a histogram of variant allele frequencies in the sample. A peak in the density plot at 0.5 indicates the sample is clonal. To get more insight in the underlying biological causes behind mutations, these can be analyzed using the R package MutationalPatterns15. Depicted here is a typical analysis producing a 96-trinucleotide plot (Figure 6). In addition to quantification of different mutation types, signature extraction can be also performed with this tool.
Constructing a developmental lineage tree 
Mutations shared amongst clones or present in a clone (and at low VAF) in the germline control are validated using IGV. Mutations are considered true when present in the sample and not at high VAF levels in the germline (Figure 7A). Mutations are considered false when not present in IGV, which can happen in poorly mapped regions (Figure 7B). In other cases, events detected by SNVFI are missed germline mutations (Figure 7C). Independent re-sequencing of mutations by targeted re-sequencing is highly recommended for these mutations in selected clones. &#160;After detection of shared somatic mutations between clones, a binary matrix is generated (step 10.8). A heatmap is constructed containing cells with and without the shared mutations A-M. Above this heatmap the developmental lineage tree is indicated (Figure 8).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59846/59846fig1.jpg" /> 
Figure 1: Flowchart depicting experimental procedure based on input material. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/59846/59846fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59846/59846fig2.jpg" /> 
Figure 2: Cell sorting strategy. First, gating is performed on small mononuclear cells. Second, single cells are gated by selection of the linear fraction. Lineage negative cells are gated. All CD34+ CD38- CD45- cells are single cell-sorted. The fraction of cells in brown should be noted, which are the sorted cells highlighted by the option &#8220;index sorting&#8221;. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/59846/59846fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59846/59846fig3.jpg" /> 
Figure 3: Representative cell culture results. Representative HSPC clones in a 384 well plate at (A) 2 weeks after plating and (B) 4 weeks after plating. (C) MSC culture after 2 weeks of medium replacement. Scale bar = 100 &#956;m. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/59846/59846fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/59846/59846fig4.jpg" /> 
Figure 4: Karyotypes. (A) Clonal HSPC culture and (B) MSC bulk sample. The karyotypes were determined by read-depth analysis. Both graphs indicate a karyotypically normal sample. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/59846/59846fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/59846/59846fig5.jpg" /> 
Figure 5: Histogram of variant allele frequencies. Histogram of variant allele frequencies of the variants in a clone before the last filtering step of SNVFI (VAF &#62;0.3). A peak at VAF = 0.5 indicates that the sample is clonal. The subclonal mutations with low VAF are excluded during last filtering step of SNVFI (VAF &#62;0.3). <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/59846/59846fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/59846/59846fig6.jpg" /> 
Figure 6: Representative mutational spectrum analysis of somatic mutations in a HSPC sample. Depicted is the relative contribution of each trinucleotide change (of which the middle base is mutated) to the total spectrum. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/59846/59846fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/59846/59846fig7.jpg" /> 
Figure 7: Manual inspection of mutations using IGV16. (A) Mutations are considered true when present in the clone and not in the bulk sample. (B) Mutations are considered as false positives when present in a poorly mapped region. (C) Mutations are considered as false positives when present in a germline control. The vertical line indicates the position of a called mutation. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/59846/59846fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/59846/59846fig8.jpg" /> 
Figure 8: Construction of a developmental lineage tree. Depicted is a dendrogram indicating developmental lineages splitting off during development. The heatmap under the dendrogram indicates the presence of mutations in different clones. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/59846/59846fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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Characterizing Mutational Load and Clonal Composition of Human Blood

Somatic mutation patterns in cells reflect previous mutagenic exposure and can reveal developmental lineage relationships. Presented here is a methodology to catalogue and analyze somatic mutations in individual hematopoietic stem and progenitor cells.

Hematopoietic stem and progenitor cells (HSPCs) gradually accumulate DNA mutations during a lifespan, which can contribute to age-associated diseases such ...

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7.3K Views.  Princess Máxima Center for Pediatric Oncology. Somatic mutation patterns in cells reflect previous mutagenic exposure and can reveal developmental lineage relationships. Presented here is a methodology to catalogue and analyze somatic mutations in individual hematopoietic stem and progenitor cells.

Video: Characterizing Mutational Load and Clonal Composition of Human Blood

Immunoglobulin Gene Sequence Analysis In Chronic Lymphocytic Leukemia: From Patient Material To Sequence Interpretation

During B cell maturation, the complex process of immunoglobulin (IG) gene V(D)J recombination coupled with somatic hypermutation (SHM) gives rise to a unique DNA sequence within each individual B cell. Since B cell malignancies result from the clonal expansion of a single cell, IG genes represent a unique molecular signature common to all the malignant cells within an individual patient; thus, IG gene rearrangements can be used as clonal markers. In addition to serving as an important clonal identifier, the IG gene sequence can act as a &#39;molecular timeline&#39; since it is associated with specific developmental stages and hence reflects the history of the B cell involved in the neoplastic transformation. Moreover, for certain malignancies, in particular chronic lymphocytic leukemia (CLL), the IG gene sequence holds prognostic and potentially predictive capabilities. That said, extrapolating meaningful conclusions from IG gene sequence analysis would be impossible if robust methods and tools were not available to aid in their analysis. This article, drawing on the vast experience of the European Research Initiative on CLL (ERIC), details the technical aspects and essential requirements necessary to ensure reliable and reproducible IG gene sequence analysis in CLL, a test that is now recommended for all CLL patients prior to treatment. More specifically, the various analytical stages are described ranging from the identification of the clonotypic IG gene rearrangement and the determination of the nucleotide sequence to the accurate clinical interpretation of the IG gene sequence data.

Herein, we present a protocol that details the technical aspects and essential requirements to ensure robust IG gene sequence analysis in patients with chronic lymphocytic leukemia (CLL), based on the accumulated experience of the European Research initiative on CLL (ERIC).

During B cell maturation, the complex process of immunoglobulin (IG) gene V(D)J recombination coupled with somatic hypermutation (SHM) gives rise to a ...

Cancer Research

Measuring Microbial Mutation Rates with the Fluctuation Assay

Fluctuation assays are widely used for estimating mutation rates in microbes growing in liquid environments. Many cultures are each inoculated with a few thousand cells, each sensitive to a selective marker that can be assayed phenotypically. These parallel cultures grow for many generations in the absence of the phenotypic marker. A subset of cultures is used to estimate the total number of cells at risk of mutations (i.e., the population size at the end of the growth period, or Nt). The remaining cultures are plated onto the selective agar. The distribution of observed resistant mutants among parallel cultures is then used to estimate the expected number of mutational events, m, using a mathematical model. Dividing m by Nt gives the estimate of the mutation rate per locus per generation. The assay has three critical aspects: the chosen phenotypic marker, the chosen volume of parallel cultures, and ensuring that the surface on the selective agar is completely dry before the incubation. The assay is relatively inexpensive and only needs standard laboratory equipment. It is also less laborious than alternative approaches, such as mutation accumulation and single-cell assays. The assay works on organisms that go through many generations rapidly and it depends on assumptions about the fitness effects of markers and cell death. However, recently developed tools and theoretical studies mean these issues can now be addressed analytically. The assay allows mutation rate estimation of different phenotypic markers in cells with different genotypes growing in isolation or in a community. By conducting multiple assays in parallel, assays can be used to study how an organism&#39;s environmental context affects spontaneous mutation rate, which is crucial for understanding antimicrobial resistance, carcinogenesis, aging, and evolution.

Here, a protocol is presented to perform a fluctuation assay and estimate microbial mutation rate using phenotypic markers. This protocol will enable researchers to assay mutations in diverse microbes and environments, determining how genotype and ecological context affect spontaneous mutation rates.

Fluctuation assays are widely used for estimating mutation rates in microbes growing in liquid environments. Many cultures are each inoculated with a few ...

Identifying DNA Mutations in Purified Hematopoietic Stem/Progenitor Cells

In recent years, it has become apparent that genomic instability is tightly related to many developmental disorders, cancers, and aging. Given that stem cells are responsible for ensuring tissue homeostasis and repair throughout life, it is reasonable to hypothesize that the stem cell population is critical for preserving genomic integrity of tissues. Therefore, significant interest has arisen in assessing the impact of endogenous and environmental factors on genomic integrity in stem cells and their progeny, aiming to understand the etiology of stem-cell based diseases.
LacI transgenic mice carry a recoverable &#955;&#160;phage vector encoding the LacI reporter system, in which the LacI gene serves as the mutation reporter. The result of a mutated LacI gene is the production of &#946;-galactosidase that cleaves a chromogenic substrate, turning it blue. The LacI reporter system is carried in all cells, including stem/progenitor cells and can easily be recovered and used to subsequently infect E. coli. After incubating infected E. coli on agarose that contains the correct substrate, plaques can be scored; blue plaques indicate a mutant LacI gene, while clear plaques harbor wild-type. The frequency of blue (among clear) plaques indicates the mutant frequency in the original cell population the DNA was extracted from. Sequencing the mutant LacI gene will show the location of the mutations in the gene and the type of mutation.
The LacI transgenic mouse model is well-established as an in vivo mutagenesis assay. Moreover, the mice and the reagents for the assay are commercially available. Here we describe in detail how this model can be adapted to measure the frequency of spontaneously occurring DNA mutants in stem cell-enriched Lin-IL7R-Sca-1+cKit++(LSK) cells and other subpopulations of the hematopoietic system.

Here we describe an in vivo mutagenesis assay for small numbers of purified hematopoietic cells using the LacI transgenic mouse model.&#160;The LacI gene can be isolated to determine the frequency, location, and type of DNA mutants&#160;spontaneously arisen or after exposure to genotoxins.

In recent years, it has become apparent that genomic instability is tightly related to many developmental disorders, cancers, and aging. Given that stem ...

Immunology and Infection

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Assessing intra-tumoral heterogeneity (ITH) is of paramount importance to anticipate failure of targeted therapies and design accordingly effective anti-tumor strategies. Although concerns are frequently raised due to differences in sample processing and depth of coverage, next-generation sequencing of solid tumors have unraveled a highly variable degree of ITH across tumor types. Capturing the genetic relatedness between primary and metastatic lesions through the identification of clonal and subclonal populations is critical to the design of therapies for advance-stage diseases. Here, we report a method for comparative lesions analysis that allows for the identification of clonal and subclonal populations among different specimens from the same patient. The experimental approach described here integrates three well-established approaches: histological analysis, high-coverage multi-lesion sequencing, and immunophenotypic analyses. In order to minimize the effects on the detection of subclonal events by inappropriate sample processing, we subjected tissues to careful pathological examination and neoplastic cell enrichment. Quality controlled DNA from neoplastic lesions and normal tissues was then subjected to high coverage sequencing, targeting the coding regions of 409 relevant cancer genes. While only looking at a limited genomic space, our approach enables evaluating the extent of heterogeneity among somatic alterations (single-nucleotide mutations and copy-number variations) in distinct lesions from a given patient. Through comparative analysis of sequencing data, we were able to distinguish clonal vs. subclonal alterations. The majority of ITH is often ascribed to passenger mutations; therefore, we also used immunohistochemistry to predict functional consequences of mutations. While this protocol has been applied to a specific tumor type, we anticipate that the methodology described here is broadly applicable to other solid tumor types.

This article describes a method to identify clonal and subclonal alterations among different specimens from a given patient. Although the experiments described here focus on a specific tumor type, the approach is broadly applicable to other solid tumors.

Assessing intra-tumoral heterogeneity (ITH) is of paramount importance to anticipate failure of targeted therapies and design accordingly effective ...

Behavior

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

Understanding tumor clonality is critical to understanding the mechanisms involved in tumorigenesis and disease progression. In addition, understanding the clonal composition changes that occur within a tumor in response to certain micro-environment or treatments may lead to the design of more sophisticated and effective approaches to eradicate tumor cells. However, tracking tumor clonal sub-populations has been challenging due to the lack of distinguishable markers. To address this problem, a VDJ-seq protocol was created to trace the clonal evolution patterns of diffuse large B cell lymphoma (DLBCL) relapse by exploiting VDJ recombination and somatic hypermutation (SHM), two unique features of B cell lymphomas.
In this protocol, Next-Generation sequencing (NGS) libraries with indexing potential were constructed from amplified rearranged immunoglobulin heavy chain (IgH) VDJ region from pairs of primary diagnosis and relapse DLBCL samples. On average more than half million VDJ sequences per sample were obtained after sequencing, which contain both VDJ rearrangement and SHM information. In addition, customized bioinformatics pipelines were developed to fully utilize sequence information for the characterization of IgH-VDJ repertoire within these samples. Furthermore, the pipeline allows the reconstruction and comparison of the clonal architecture of individual tumors, which enables the examination of the clonal heterogeneity within the diagnosis tumors and deduction of clonal evolution patterns between diagnosis and relapse tumor pairs. When applying this analysis to several diagnosis-relapse pairs, we uncovered key evidence that multiple distinctive tumor evolutionary patterns could lead to DLBCL relapse. Additionally, this approach can be expanded into other clinical aspects, such as identification of minimal residual disease, monitoring relapse progress and treatment response, and investigation of immune repertoires in non-lymphoma contexts.

This protocol describes an approach to interrogate the recombined immunoglobulin heavy chain VDJ regions of lymphomas by deep-sequencing and retrieve VDJ rearrangement and somatic hypermutation status to delineate clonal architecture of individual tumor. Comparing clonal architecture between paired diagnosis and relapse samples reveals lymphoma relapse clonal evolution modes.

Understanding tumor clonality is critical to understanding the mechanisms involved in tumorigenesis and disease progression. In addition, understanding ...

Medicine

Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis

Monocytes are key contributors in various inflammatory disorders and alterations to these cells, including their subset proportions and functions, can have pathological significance. An ideal method for examining alterations to monocytes is whole blood flow cytometry as the minimal handling of samples by this method limits artifactual cell activation. However, many different approaches are taken to gate the monocyte subsets leading to inconsistent identification of the subsets between studies. Here we demonstrate a method using whole blood flow cytometry to identify and characterize human monocyte subsets (classical, intermediate, and non-classical). We outline how to prepare the blood samples for flow cytometry, gate the subsets (ensure contaminating cells have been removed), and determine monocyte subset expression of surface markers &#8212; in this example M1 and M2 markers. This protocol can be extended to other studies that require a standard gating method for assessing monocyte subset proportions and monocyte subset expression of other functional markers.

Here we present a protocol for characterizing monocyte subsets by whole blood flow cytometry. This includes outlining how to gate the subsets and assess their expression of surface markers and giving an example of the assessment of the expression of M1 (inflammatory) and M2 markers (anti-inflammatory).

Monocytes are key contributors in various inflammatory disorders and alterations to these cells, including their subset proportions and functions, can ...

Analysis of Somatic Hypermutation in the JH4 intron of Germinal Center B cells from Mouse Peyer's Patches

Within the germinal centers of lymphoid organs, mature B cells alter their expressed immunoglobulin (Ig) by introducing untemplated mutations into the variable coding exons of the Ig heavy and light chain gene loci. This process of somatic hypermutation (SHM) requires the enzyme activation-induced cytidine deaminase (AID), which converts deoxycytidines (C), into deoxyuridines (U). Processing the AID-generated U:G mismatches into mutations by the base excision and mismatch repair pathways introduces new Ig coding sequences that may produce a higher affinity Ig. Mutations in AID or DNA repair genes can block or significantly alter the types of mutations observed in the Ig loci. We describe a protocol to quantify JH4 intron mutations that uses fluorescence activated cell sorting (FACS), PCR, and Sanger sequencing. Although this assay does not directly measure Ig affinity maturation, it is indicative of mutations in Ig variable coding sequences. Additionally, these methods utilize common molecular biology techniques which&#160;analyze&#160;mutations in Ig sequences of multiple B cell clones. Thus, this assay is an invaluable tool in the study of SHM and Ig diversification.

Presented here is an assay to quantify somatic hypermutation within the immunoglobulin heavy chain gene locus using germinal center B cells from mouse Peyer&#8217;s patches.

Within the germinal centers of lymphoid organs, mature B cells alter their expressed immunoglobulin (Ig) by introducing untemplated mutations into the ...

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

Immunological memory, the hallmark of adaptive immunity, is orchestrated by T and B lymphocytes. In circulation and different organs, there are billions of unique T and B cell clones, and each one can bind a specific antigen, leading to proliferation, differentiation and/or cytokine secretion. The vast heterogeneity in T and B cells is generated by random recombination of different genetic segments. Next-generation sequencing (NGS) technologies, developed in the last decade, enable an unprecedented in-depth view of the T and B cell receptor immune repertoire. Studies in various inflammatory conditions, immunodeficiencies, infections and malignancies demonstrated marked changes in clonality, gene usage, and biophysical properties of immune repertoire, providing important insights about the role of adaptive immune responses in different disorders.
Here, we provide a detailed protocol for NGS of immune repertoire of T and B cells from blood and tissue. We present a pipeline starting from DNA isolation through library preparation, sequencing on NGS sequencer and ending with basic analyses. This method enables exploration of specific T and B cells at the nucleotide or amino-acid level, and thus can identify dynamic changes in lymphocyte populations and diversity parameters in different diseases. This technique is slowly entering clinical practice and has the potential for identification of novel biomarkers, risk stratification and precision medicine.

The current protocol describes a method for DNA isolation from blood samples and intestinal biopsies, generation of TCR&#946; and IGH PCR libraries for next-generation sequencing, performance of a NGS run and basic data analysis.

Immunological memory, the hallmark of adaptive immunity, is orchestrated by T and B lymphocytes. In circulation and different organs, there are billions ...

Bone Marrow Transplantation Procedures in Mice to Study Clonal Hematopoiesis

Clonal hematopoiesis is a prevalent age-associated condition that results from the accumulation of somatic mutations in hematopoietic stem and progenitor cells (HSPCs). Mutations in driver genes, that confer cellular fitness, can lead to the development of expanding HSPC clones that increasingly give rise to progeny leukocytes harboring the somatic mutation. Because clonal hematopoiesis has been associated with heart disease, stroke, and mortality, the development of experimental systems that model these processes is key to understanding the mechanisms that underly this new risk factor. Bone marrow transplantation procedures involving myeloablative conditioning in mice, such as total-body irradiation (TBI), are commonly employed to study the role of immune cells in cardiovascular diseases. However, simultaneous damage to the bone marrow niche and other sites of interest, such as the heart and brain, is unavoidable with these procedures. Thus, our lab has developed two alternative methods to minimize or avoid possible side effects caused by TBI: 1) bone marrow transplantation with irradiation shielding and 2) adoptive BMT to non-conditioned mice. In shielded organs, the local environment is preserved allowing for the analysis of clonal hematopoiesis while the function of resident immune cells is unperturbed. In contrast, the adoptive BMT to non-conditioned mice has the additional advantage that both the local environments of the organs and the hematopoietic niche are preserved. Here, we compare three different hematopoietic cell reconstitution approaches and discuss their strengths and limitations for studies of clonal hematopoiesis in cardiovascular disease.

We describe three methods of bone marrow transplantation (BMT): BMT with total-body irradiation, BMT with shielded irradiation, and BMT method with no pre-conditioning (adoptive BMT) for the study of clonal hematopoiesis in mouse models.

Clonal hematopoiesis is a prevalent age-associated condition that results from the accumulation of somatic mutations in hematopoietic stem and progenitor ...

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Few techniques can assess phenotype and fate for the same cell simultaneously. Most of the current protocols used to characterize phenotype, although able to generate large datasets, necessitate the destruction of the cell of interest, making it impossible to assess its functional fate. Heterogeneous biological differentiating systems like hematopoiesis are therefore difficult to describe. Building on cell division tracking dyes, we further developed a protocol to simultaneously determine kinship, division number, and differentiation status for many single hematopoietic progenitors. This protocol allows the assessment of the ex vivo differentiation potential of murine and human hematopoietic progenitors, isolated from various biological sources. Moreover, as it is based on flow cytometry and a limited number of reagents, it can quickly generate a large amount of data, at the single-cell level, in a relatively inexpensive manner. We also provide the analytical pipeline for single-cell analysis, combined with a robust statistical framework. As this protocol allows the linking of cell division and differentiation at the single-cell level, it can be used to quantitatively assess symmetric and asymmetric fate commitment, the balance between self-renewal and differentiation, and the number of divisions for a given commitment fate. Altogether, this protocol can be used in experimental designs aiming to unravel the biological differences between hematopoietic progenitors, from a single-cell perspective.

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Presented here is a flow cytometry-based technique that allows for simultaneously measuring the number of cell divisions, surface cell phenotype, and cellular kinship. Those properties can be tested statistically using a permutation-based framework.

Few techniques can assess phenotype and fate for the same cell simultaneously. Most of the current protocols used to characterize phenotype, although able ...

Characterizing Mutational Load and Clonal Composition of Human Blood

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Immunoglobulin Gene Sequence Analysis In Chronic Lymphocytic Leukemia: From Patient Material To Sequence Interpretation

Measuring Microbial Mutation Rates with the Fluctuation Assay

Identifying DNA Mutations in Purified Hematopoietic Stem/Progenitor Cells

Comparative Lesions Analysis Through a Targeted Sequencing Approach

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis

Analysis of Somatic Hypermutation in the JH4 intron of Germinal Center B cells from Mouse Peyer's Patches

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

Bone Marrow Transplantation Procedures in Mice to Study Clonal Hematopoiesis

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells