bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2020.05.21.109314

Rolling Signal-based Ripley's K: A new algorithm to identify spatial patterns in histological specimens

Healy, C. P.·Adler, F. R.·Deans, T. L.
View original source ↗Export metadata ↓

Abstract

The spatial distribution of cells within a tissue underlies organ function. However, these spatial distributions are often difficult to identify, making it challenging to evaluate how cells establish these patterns during development or how diseases may disrupt these patterns and impair function. To address this, we developed an image analysis tool based on a novel algorithm that identifies spatial patterns within tissues. This analytical tool was used to study the bone marrow, a specialized microenvironment in which spatial patterning of regulatory cells may influence the differentiation and survival of hematopoietic stem cells. Using this algorithm, we discovered clusters of regulatory cells within the bone marrow that suggest an organization of micro-niches, which may form the basis of the hematopoietic stem cell microenvironment. This work provides a new tool for the detection and analysis of tissue morphology that enables identification of spatial patterns within tissues that can lead to a deeper understanding of tissue function, provide clues for early onset of disease, and be used as a tool for studying the impact of pharmaceutics on tissue development and regeneration. In BriefThis work introduces a new statistic to analyze the patterning of cells and physiological features in histological images. This statistic was used on a published set of immunofluorescent images of murine bone to identify novel spatial structures within the bone marrow that may provide new inisghts to the organization of the hematopoietic stem cell microenvironment. HighlightsO_LIRSRK, a statistical tool for analyzing the spatial distribution of features in histological images, is introduced. C_LIO_LIRSRK incorporates the quantification of signal distribution to identify unique spatial patterns. C_LIO_LISpatial patterns in hematopoietic stem cell microenvironments are identified. C_LI Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

Explore related subjects

systems biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Healy, C. P., Adler, F. R., Deans, T. L.. 2020-05-24. Rolling Signal-based Ripley's K: A new algorithm to identify spatial patterns in histological specimens. https://doi.org/10.1101/2020.05.21.109314

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

A three-factor model can explain symmetry breaking and the n-1 rule during X-chromosome inactivation

X-chromosome inactivation (XCI) is a mammalian gene regulatory process that via chromosome-scale transcriptional silencing, leaves active a single X chromosome per diploid genome complement. The mechanisms that enable cells to select the appropriate number of X chromosomes to inactivate while leaving others active remain unclear. We demonstrate that all known chromosomal counting scenarios during XCI can be accounted for by a three-factor mathematical model that includes (1) Xist, the long noncoding RNA (lncRNA) responsible for gene silencing during XCI in eutherians, (2) an X-linked transcriptional activator of Xist, and (3) the autosomally-encoded SPEN, an Xist RNA-binding protein that is essential for gene silencing during XCI. In our model, symmetry between active X chromosomes is broken by stochastic differences in the number of Xist molecules that associate with the X chromosomes at the onset of XCI. Subsequently, integrated positive and negative feedback loops amplify then propagate superstoichiometric assemblies of SPEN complexed with Xist on the inactive chromosome(s), and select for levels of X-linked activator that enable only one X chromosome per diploid genome complement to remain active. Thus, asymmetric propagation of the SPEN-Xist ribonucleoprotein complex may play an essential role in symmetry breaking and chromosomal counting during XCI, independently of or in concert with cis-acting networks of DNA regulatory elements. Our results also have conceptual implications for gene regulatory circuits on autosomes.

systems biology↗
bioRxiv2026

stem.MAP: Identification of cell-type-specific protein complexes in mammalian embryonic stem cells.

Protein complexes carry out critical functions during development. Mapping cell-type-specific protein complexes in early embryogenesis is essential for uncovering the molecular mechanisms of embryonic development. We unfortunately currently lack a catalog of protein complexes specific for embryogenesis including embryonic stem cells. Here we develop a proteomics-based machine learning workflow to predict protein complexes present in mouse embryonic stem cells (mESC). Our method integrates data from a cell-type agnostic protein complex resource with co-fractionation mass spectrometry (CF-MS) experiments on mESC. Our resource, stem.MAP, identifies 3,986 complexes encompassing ~133,000 unique interactions, and 8,616 proteins. stem.MAP captures variant complexes critical for stem cell function including the PRC2, BAF and NuRD complexes. stem.MAP also discriminates mutually exclusive interactions specific to embryonic stem cells (e.g. Eif4ebp2 - Eif4e). Finally we observe 1,388 high confidence stem.MAP interactions found only in mESC CF-MS data, many with support from AlphaFold modeling and a gastruloid coregulation network. We make stem.MAP protein complexes available through our web resource: stem.proteincomplexes.org. We anticipate stem.MAP will be beneficial to the study of early development and we expect our proteomics-based machine learning workflow to be applicable to the creation of any context-specific protein interaction network.

systems biology↗
bioRxiv2026

Coarse-graining genome-scale metabolic models for dynamic, multi-scale simulations of Pseudomonas aeruginosa in gel environments

Microbial communities are shaped by coupled interactions between microenvironment conditions and the intracellular adaptations that emerge in response external metabolic cues. For pathogenic microbes such as Pseudomonas aeruginosa, these interactions can rapidly generate heterogeneous sub-populations that exhibit adaptations for sustaining host infection and resisting antibiotic treatment. Technological strides in -omics data collection, combined with robust computational tools like genome-scale metabolic models (GEM) for predicting cellular metabolism, have yielded promising multi-scale depictions of microbial population development in simulated spatial environments. These multi-scale model simulations help establish and evaluate mechanistic contexts for how infection-enhancing behaviors might emerge in the population or be influenced by variable perturbations in the biological system. However, the number and rigorous tuning of parameters influencing the connections between intracellular-extracellular events pose barriers when shaping simulations to different in vitro systems of interest. In this work, we present a genome-scale, metabolism-informed continuum model (GEM-CM), a computational framework for coupling a reaction-diffusion model to a surrogate GEM representation that generates metabolic rate predictions informed by flux balance analysis outputs. A key feature of GEM-CM is its application of random forest regression, or ensembles, to coarse-grain the interface connecting the GEM and reaction-diffusion models down to a small set of input metabolites methodically chosen through the random forests evaluation of feature importance. To demonstrate GEM-CMs utility, we evaluated simulation predictions against P. aeruginosa PA14 populations radially propagating in low weight percentage agar gels. In addition to biomass growth, we simulated PA14 populations producing the cytotoxic secondary metabolite pyocyanin (PCN). GEM-CM could capture PA14 growth dynamics in agar gels when ensembles were provided with three carbon source inputs describing the simulated growth media. Bacterial motility parameters and the random forest ensemble allowed GEM-CM simulations to accurately predict spatial biomass distributions for PA14 populations over time and probe their PCN production dynamics. Ensembles also demonstrated robust performance for single gene knockout conditions with GEM-CM simulation accurately capturing the partial or total biomass and PCN inhibition observed in transposon mutant screenings. When evaluating transposon mutant population development, the GEM-CM framework additionally suggested that metH may be a promising target to inhibit the spatial propagation of PA14. This works proof-of-concept applications illustrated GEM-CM utility as an early stage multi-scale modeling tool for interrogating in vitro metabolic behavior in microbial populations.

systems biology↗