Ptk7 Marks the First Human Developmental EMT <em>In Vitro</em>

<div><p>Epithelial to mesenchymal transitions (EMTs) are thought to be essential to generate diversity of tissues during early fetal development, but these events are essentially impossible to study at the molecular level <em>in vivo</em> in humans. The first EMT event that has been described morphologically in human development occurs just prior to generation of the primitive streak. Because human embryonic stem cells (hESCs) and induced pluripotent stem cells (hiPSCs) are thought to most closely resemble cells found in epiblast-stage embryos prior to formation of the primitive streak, we sought to determine whether this first human EMT could be modeled <em>in vitro</em> with pluripotent stem cells. The data presented here suggest that generating embryoid bodies from hESCs or hiPSCs drives a procession of EMT events that can be observed within 24–48 hours after EB generation. These structures possess the typical hallmarks of developmental EMTs, and portions also display evidence of primitive streak and mesendoderm. We identify PTK7 as a novel marker of this EMT population, which can also be used to purify these cells for subsequent analyses and identification of novel markers of human development. Gene expression analysis indicated an upregulation of EMT markers and ECM proteins in the PTK7+ population. We also find that cells that undergo this developmental EMT retain developmental plasticity as sorting, dissociation and re-plating reestablishes an epithelial phenotype.</p> </div

PTK7+ population displays mesenchymal markers but not epithelial markers.

<p>(A) Epithelial and mesenchymal markers were mutually exclusive in hEBs. Immunofluorescence staining of H9 hEB serial sections. Serial sections of H9 Day5 hEBs were stained with epithelial markers (E-CADHERIN) and mesenchymal markers (N-CADHERIN). (B), PTK7+ population displayed mesenchymal markers but not epithelial markers. Immunofluorescence staining of H9 hEB serial sections. Serial sections of H9 24 hr hEBs were stained with PTK7 (green), epithelial marker (E-CADHERIN, red) and mesenchymal markers (N-CADHERIN, red; VIMENTIN, green). The rightmost column shows the merged images from two fluorescent channels and DAPI. All images taken at 20X.</p

Lineage marker analysis on PTK7− and PTK7+ populations.

<p>Feeder-free H9 and XFiPSC2 (differentiated for 2 days) were sorted into PTK7+ and PTK7<b>−</b> populations. Expression level was determined by microarray analysis as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050432#s2" target="_blank">Materials and Methods</a>. All samples were normalized to PTK7<b>−</b> from the same sort. Shown are the averages of normalized readings for PTK7+ and PTK7<b>−</b>, with standard errors across two experiments. The relative expression of all samples was analyzed for (A) ectoderm markers, (B) endoderm markers and (C) mesoderm markers.</p

EMT marker and ECM protein analysis on undifferentiated hPSC, PTK7−, PTK7+ and DE populations.

<p>Feeder-free H9 and XFiPSC2 (differentiated for 2 days) were sorted into PTK7+ and PTK7<b>−</b> populations. Expression level was determined by microarray analysis as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050432#s2" target="_blank">Materials and Methods</a>. All samples were normalized to PTK7<b>−</b> from the same sort. Shown are the averages of normalized readings for PTK7+ and PTK7<b>−</b>, with standard error across two experiments. The relative expression of all samples was analyzed for (A) EMT markers, (B) ECM proteins and (C) genes that are upregulated in PTK7+ samples.</p

PTK7 marked cells that underwent EMT in adherent differentiation of hPSCs.

<p>Immunofluorescence staining of adherent XFiPSC2. White boundary indicates the PTK7 population. XFiPSC2 were plated on coverslips and fixed before immunostaining with PTK7 (green), epithelial maker (E-CADHERIN, red) and mesenchymal marker (VIMENTIN, white). Top row: undifferentiated XFiPSC2. Bottom row: XFiPSC2 differentiated in low FGF conditions for 2 days. The 4^th column shows the merged images from three fluorescent channels and DAPI. All images taken at 10X.</p

Pluripotency, lineage, and viability marker analysis of PTK7 population in hEBs.

<p>Immunofluorescence staining of XFiPSC2 hEB serial sections. White boundary indicates the PTK7 population. In cases where there were no PTK7 staining in a particular section, the boundary was extrapolated from adjacent sections. XFiPSC2 hEBs were cultured for 24 hrs before cryosectioned. Serial sections of XFiPSC2 24 hr hEBs were stained with PTK7 (green), epithelial markers (E-CADHERIN, red; EPCAM, green), mesenchymal markers (N-CADHERIN, red; VIMENTIN, white), pluripotency markers (OCT4, SOX2, NANOG, red), developmental genes (LIN28A, LIN28B, HMGA2, red), primitive streak markers (SLUG, red (arrows); Brachyury, white) and endodermal markers (SOX17, green; EOMES (arrowheads), red; FOXA2, white). The 4^th column shows the merged images from three fluorescent channels and DAPI. All images taken at 20X.</p

Cell Fate Determination on PTK7− and PTK7+ populations.

<p>Feeder-free H9 (differentiated for 2 days) were sorted into PTK7+ and PTK7<b>−</b> populations. The two populations were re-aggregated as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050432#s2" target="_blank">Materials and Methods</a>, and replated onto Matrigel-coated coverslips. The PTK7<b>−</b> and PTK7+ re-aggregates were cultured in PSC media for 2 days, and then differentiated using established protocols. (A) Epithelial and mesenchymal marker analysis in PTK7<b>−</b> and PTK7+ re-aggregates 3 day post-sorting. PTK7+ cells that underwent developmental EMT possessed plasticity to revert back to an epithelial cell type. Undifferentiated H9 were plated on coverslips and fixed 4 days after passage. PTK7<b>−</b> and PTK7+ re-aggregates were plated on coverslips and fixed after 2 days in culture. Undifferentiated H9 (top row), PTK7<b>−</b> re-aggregate (mid row) and PTK7+ re-aggregate (bottom row) were stained for PTK7 (green), epithelial maker (E-CADHERIN, red) and mesenchymal marker (VIMENTIN, white). The 4^th column shows the merged images from three fluorescent channels and DAPI. (B) PTK7+ and PTK7<b>−</b> re-aggregates exhibited comparable definitive endoderm formation. Feeder-free H9, PTK7<b>−</b> and PTK7+ re-aggregates were induced to form definitive endoderm with ActivinA treatment as described. Coverslips were fixed and immunostained with endodermal markers EOMES (red) and SOX17 (green). The 3^rd column shows the merged images from two fluorescent channels and DAPI. (C) PTK7<b>−</b> and PTK7+ re-aggregates exhibited comparable neural differentiation. XFiPSC2, PTK7<b>−</b> and PTK7+ re-aggregates were induced to form neural rosettes as previously described. Coverslips were fixed and immunostained with neural markers SOX1 (red), SOX2 (green) and NESTIN (white). The 4^th column shows the merged images from three fluorescent channels and DAPI. All images taken at 10X.</p

Evaluating Automatic Segmentation for Swallowing-Related Organs for Head and Neck Cancer

Purpose: To evaluate the accuracy of deep-learning-based auto-segmentation of the superior constrictor, middle constrictor, inferior constrictor, and larynx in comparison with a traditional multi-atlas-based method. Methods and Materials: One hundred and five computed tomography image datasets from 83 head and neck cancer patients were retrospectively collected and the superior constrictor, middle constrictor, inferior constrictor, and larynx were analyzed for deep-learning versus multi-atlas-based segmentation. Eighty-three computed tomography images (40 diagnostic computed tomography and 43 planning computed tomography) were used for training the convolutional neural network, and for atlas-based model training. The remaining 22 computed tomography datasets were used for validation of the atlas-based auto-segmentation versus deep-learning-based auto-segmentation contours, both of which were compared with the corresponding manual contours. Quantitative measures included Dice similarity coefficient, recall, precision, Hausdorff distance, 95th percentile of Hausdorff distance, and mean surface distance. Dosimetric differences between the auto-generated contours and manual contours were evaluated. Subjective evaluation was obtained from 3 clinical observers to blindly score the autosegmented structures based on the percentage of slices that require manual modification. Results: The deep-learning-based auto-segmentation versus atlas-based auto-segmentation results were compared for the superior constrictor, middle constrictor, inferior constrictor, and larynx. The mean Dice similarity coefficient values for the 4 structures were 0.67, 0.60, 0.65, and 0.84 for deep-learning-based auto-segmentation, whereas atlas-based auto-segmentation has Dice similarity coefficient results at 0.45, 0.36, 0.50, and 0.70, respectively. The mean 95th percentile of Hausdorff distance (cm) for the 4 structures were 0.41, 0.57, 0.59, and 0.54 for deep-learning-based auto-segmentation, but 0.78, 0.95, 0.96, and 1.23 for atlas-based auto-segmentation results, respectively. Similar mean dose differences were obtained from the 2 sets of autosegmented contours compared to manual contours. The dose-volume discrepancies and the average modification rates were higher with the atlas-based auto-segmentation contours. Conclusion: Swallowing-related structures are more accurately generated with DL-based versus atlas-based segmentation when compared with manual contours