Cell Lineage Analysis of the Mammalian Female Germline

Fundamental aspects of embryonic and post-natal development, including maintenance of the mammalian female germline, are largely unknown. Here we employ a retrospective, phylogenetic-based method for reconstructing cell lineage trees utilizing somatic mutations accumulated in microsatellites, to study female germline dynamics in mice. Reconstructed cell lineage trees can be used to estimate lineage relationships between different cell types, as well as cell depth (number of cell divisions since the zygote). We show that, in the reconstructed mouse cell lineage trees, oocytes form clusters that are separate from hematopoietic and mesenchymal stem cells, both in young and old mice, indicating that these populations belong to distinct lineages. Furthermore, while cumulus cells sampled from different ovarian follicles are distinctly clustered on the reconstructed trees, oocytes from the left and right ovaries are not, suggesting a mixing of their progenitor pools. We also observed an increase in oocyte depth with mouse age, which can be explained either by depth-guided selection of oocytes for ovulation or by post-natal renewal. Overall, our study sheds light on substantial novel aspects of female germline preservation and development

Muscle-Bound Primordial Stem Cells Give Rise to Myofiber-Associated Myogenic and Non-Myogenic Progenitors

Myofiber cultures give rise to myogenic as well as to non-myogenic cells. Whether these myofiber-associated non-myogenic cells develop from resident stem cells that possess mesenchymal plasticity or from other stem cells such as mesenchymal stem cells (MSCs) remain unsolved. To address this question, we applied a method for reconstructing cell lineage trees from somatic mutations to MSCs and myogenic and non-myogenic cells from individual myofibers that were cultured at clonal density

Comparing Algorithms That Reconstruct Cell Lineage Trees Utilizing Information on Microsatellite Mutations

<div><p>Organism cells proliferate and die to build, maintain, renew and repair it. The cellular history of an organism up to any point in time can be captured by a cell lineage tree in which vertices represent all organism cells, past and present, and directed edges represent progeny relations among them. The root represents the fertilized egg, and the leaves represent extant and dead cells. Somatic mutations accumulated during cell division endow each organism cell with a genomic signature that is unique with a very high probability. Distances between such genomic signatures can be used to reconstruct an organism's cell lineage tree. Cell populations possess unique features that are absent or rare in organism populations (e.g., the presence of stem cells and a small number of generations since the zygote) and do not undergo sexual reproduction, hence the reconstruction of cell lineage trees calls for careful examination and adaptation of the standard tools of population genetics. Our lab developed a method for reconstructing cell lineage trees by examining only mutations in highly variable microsatellite loci (MS, also called short tandem repeats, STR). In this study we use experimental data on somatic mutations in MS of individual cells in human and mice in order to validate and quantify the utility of known lineage tree reconstruction algorithms in this context. We employed extensive measurements of somatic mutations in individual cells which were isolated from healthy and diseased tissues of mice and humans. The validation was done by analyzing the ability to infer known and clear biological scenarios. In general, we found that if the biological scenario is simple, almost all algorithms tested can infer it. Another somewhat surprising conclusion is that the best algorithm among those tested is Neighbor Joining where the distance measure used is normalized absolute distance. We include our full dataset in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003297#pcbi.1003297.s015" target="_blank">Tables S1</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003297#pcbi.1003297.s016" target="_blank">S2</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003297#pcbi.1003297.s017" target="_blank">S3</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003297#pcbi.1003297.s018" target="_blank">S4</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003297#pcbi.1003297.s019" target="_blank">S5</a> to enable further analysis of this data by others.</p></div

Colon Stem Cell and Crypt Dynamics Exposed by Cell Lineage Reconstruction

Stem cell dynamics in vivo are often being studied by lineage tracing methods. Our laboratory has previously developed a retrospective method for reconstructing cell lineage trees from somatic mutations accumulated in microsatellites. This method was applied here to explore different aspects of stem cell dynamics in the mouse colon without the use of stem cell markers. We first demonstrated the reliability of our method for the study of stem cells by confirming previously established facts, and then we addressed open questions. Our findings confirmed that colon crypts are monoclonal and that, throughout adulthood, the process of monoclonal conversion plays a major role in the maintenance of crypts. The absence of immortal strand mechanism in crypts stem cells was validated by the age-dependent accumulation of microsatellite mutations. In addition, we confirmed the positive correlation between physical and lineage proximity of crypts, by showing that the colon is separated into small domains that share a common ancestor. We gained new data demonstrating that colon epithelium is clustered separately from hematopoietic and other cell types, indicating that the colon is constituted of few progenitors and ruling out significant renewal of colonic epithelium from hematopoietic cells during adulthood. Overall, our study demonstrates the reliability of cell lineage reconstruction for the study of stem cell dynamics, and it further addresses open questions in colon stem cells. In addition, this method can be applied to study stem cell dynamics i

Performance summary of all the methods for datasets composed of cells of same depth only.

<p>Each column presents a different clustering measure (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003297#s4" target="_blank">Materials and Methods</a> for details), and each bar represents a different distance measure, where the colors specify the distance measures as noted in the legend. The first group of bars (from left to right) presents the results using the NJ algorithm, the second group of bars presents the results using the QMC algorithm, the third presents the results using the UPGMA algorithm, and the last one presents the results using the BATWING tool. Rows description: (A) The average score of all the methods, where higher values (that are transformations of the real scores) indicate better performance. (B) The number of times every method received the highest rank (in this case it is 31 since we compared 31 methods).</p

Performance of different methods on the same dataset.

<p>Two reconstructed lineage trees containing the same cells from three different mice are shown: M1 (turquoise), M3 (blue), and M4 (orange). (A) A tree using the NJ algorithm along with the Normalized-Absolute distance measure. (B) A tree using the NJ algorithm along with the Equal or Not distance measure. The root of the trees (colored in black) is the weighted mean of the organisms' putative zygotes. It can be seen that the performance of the Normalized-Absolute distance measure is clearly better.</p

Example of results on a few datasets.

<p>The table represents the results of the three clustering measures (QLC, TE and HS) over 10 NJ reconstructed methods. For the measure QLC higher scores mean better performance, whereas for the measures TE and HS lower values mean better performance. It can be seen that in some cases, for example dataset #7, all the distance measures (except for Euclidian) give the same best score (1 for QLC and 0 for TE and HS). For dataset #1, on the other hand, all the distance measures give a rather similar bad score. It is not surprising that the scores of a dataset which contains 9 individuals (#1) will be lower than the scores of a dataset which contains only 2 individuals (#7). There are cases, like #3 and #4, in which the best performance is achieved by one method (NJ-Normalized Absolute) and other cases, like #5, where a few methods receive a very high score (NJ-Normalized Absolute, NJ-MMM length dependent rates, and more).</p

Performance summary of all the methods on all the datasets of mice and humans.

<p>Upper panels – Mouse, Lower panels- Human. Each column presents a different clustering measure (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003297#s4" target="_blank">Materials and Methods</a> for details), and each bar represents a different distance measure, where the colors specify the distance measures as noted in the legend. The first and the second group of bars (from left to right) present the results using the NJ algorithm and the QMC algorithm respectively. Rows description: (A) The average score of all the methods, where higher values (that are transformations of the real scores) indicate better performance. (B) The number of times every method received the highest rank (for the mouse panel the highest rank is 20 since we compared 20 methods, and for the human panel the highest rank is 12).</p

Depth separation comparison of two distance measures on the same dataset.

<p>Two reconstructed lineage trees containing whole crypts from M9 (52 days) and two reconstructed lineage trees containing whole crypts from M7 (199 days) are shown. The root of the trees (colored in black) is the signature of the tail extracted from each mouse. (A) Two reconstructed trees using the NJ algorithm along with Equal or Not distance measure. (B) Two reconstructed trees using the NJ algorithm along with the Absolute distance measure.</p

Cells from different organisms are clustered separately in the lineage tree.

<p>Two reconstructed lineage trees are shown: (A) A lineage tree containing cells from three mice, M3 (blue), M5 (pink) and M6 (green). (B) A lineage tree containing cells from seven humans, H1 (blue), H2 (red), H3 (orange), H4 (pink), H5 (green), H6 (purple) and H7 (turquoise). The root of the trees (colored in black) is the weighted mean of the organisms' putative zygotes. The trees were reconstructed using the NJ algorithm along with the Normalized-Absolute distance measure.</p