Biology subjects

Lex, A.

Publications and source records attributed to Lex, A..

4 recordsLinked to original sources

bioRxiv2025

QUILPEN provides independent and label-free single-cell quantification of pigmentation dynamics and organelle content

The relationship between melanogenesis, pigmentation, and melanocyte behavior is complex. In melanocytes, pigmentation is often associated with differentiation, yet mature melanocytes vary in pigment content. In melanoma, pigmentation-linked transcriptional programs may have prognostic value, but visual assessments of tumor pigmentation have yielded inconsistent results. Progress linking pigmentation phenotypes to cell state has been limited by a lack of tools that can directly and dynamically quantify melanin content in live cells. Here we present QUantitative Imaging of Label-free Pigment-associated ENtities (QUILPEN), a label-free multi-modal imaging technique that combines quantitative phase imaging (QPI), quadrant darkfield (QDF), and absorption imaging, to independently capture light that has been transmitted, scattered, and absorbed. This non-destructive method enables live-cell imaging over multiple days without labels. We show absorption as a reliable readout of melanin content, which can be decoupled from melanosome content detected by QDF, which measures scattered light. Applying QUILPEN to melanoma cells before and during repigmentation, we find that melanin content is highly heterogeneous, and that this heterogeneity is reinstated upon repigmentation. Lineage tracking further reveals that melanin synthesis rates are heritable and can be transmitted both symmetrically and asymmetrically. QUILPEN enables real-time quantification of pigmentation dynamics and cell-level heterogeneity. SignificancePigmentation is a defining feature of melanocytic cells and linked to behavior, yet melanins optical properties complicate direct measurement with existing approaches. Taking advantage of the absorbing nature of pigment and scattering properties of organelles, we can independently track pigment and organelle content over time and along cell lineages in a label-free manner to reveal population heterogeneity and patterns of inheritance. Research HighlightsQUILPEN is a label-free, live cell imaging pipeline that independently quantifies pigment and organelles in melanocytic cells. Tracking pigment and organelle content along cell lineages reveals varied inheritance of pigment-associated features. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/683473v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@8d1ef9org.highwire.dtl.DTLVardef@ec6ebdorg.highwire.dtl.DTLVardef@5a7b64org.highwire.dtl.DTLVardef@5b0d80_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2017

Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs

The majority of diseases that are a significant challenge for public and individual heath are caused by a combination of hereditary and environmental factors. In this paper we introduce Lineage, a novel visual analysis tool designed to support domain experts who study such multifactorial diseases in the context of genealogies. Incorporating familial relationships between cases with other data can provide insights into shared genomic variants and shared environmental exposures that may be implicated in such diseases. We introduce a data and task abstraction, and argue that the problem of analyzing such diseases based on genealogical, clinical, and genetic data can be mapped to a multivariate graph visualization problem. The main contribution of our design study is a novel visual representation for tree-like, multivariate graphs, which we apply to genealogies and clinical data about the individuals in these families. We introduce data-driven aggregation methods to scale to multiple families. By designing the genealogy graph layout to align with a tabular view, we are able to incorporate extensive, multivariate attributes in the analysis of the genealogy without cluttering the graph. We validate our designs by conducting case studies with our domain collaborators.

bioinformatics↗

bioRxiv2017

Interactive Visual Exploration And Refinement Of Cluster Assignments

BackgroundWith ever-increasing amounts of data produced in biology research, scientists are in need of efficient data analysis methods. Cluster analysis, combined with visualization of the results, is one such method that can be used to make sense of large data volumes. At the same time, cluster analysis is known to be imperfect and depends on the choice of algorithms, parameters, and distance measures. Most clustering algorithms dont properly account for ambiguity in the source data, as records are often assigned to discrete clusters, even if an assignment is unclear. While there are metrics and visualization techniques that allow analysts to compare clusterings or to judge cluster quality, there is no comprehensive method that allows analysts to evaluate, compare, and refine cluster assignments based on the source data, derived scores, and contextual data.\n\nResultsIn this paper, we introduce a method that explicitly visualizes the quality of cluster assignments, allows comparisons of clustering results and enables analysts to manually curate and refine cluster assignments. Our methods are applicable to matrix data clustered with partitional, hierarchical, and fuzzy clustering algorithms. Furthermore, we enable analysts to explore clustering results in context of other data, for example, to observe whether a clustering of genomic data results in a meaningful differentiation in phenotypes.\n\nConclusionsOur methods are integrated into Caleydo StratomeX, a popular, web-based, disease subtype analysis tool. We show in a usage scenario that our approach can reveal ambiguities in cluster assignments and produce improved clusterings that better differentiate genotypes and phenotypes.

bioinformatics↗

bioRxiv2017

UpSetR: An R Package For The Visualization Of Intersecting Sets And Their Properties

Venn and Euler diagrams are a popular yet inadequate solution for quantitative visualization of set intersections. A scalable alternative to Venn and Euler diagrams for visualizing intersecting sets and their properties is needed. We developed UpSetR, an open source R package that employs a scalable matrix-based visualization to show intersections of sets, their size, and other properties. UpSetR is available at https://cran.r-project.org/package=UpSetR and released under the MIT License. A Shiny app is available at https://gehlenborglab.shinyapps.io/upsetr/.

bioinformatics↗