Spatial localisation meets biomolecular networks

Spatial organisation through localisation/compartmentalisation of species is a ubiquitous but poorly understood feature of cellular biomolecular networks. Current technologies in systems and synthetic biology (spatial proteomics, imaging, synthetic compartmentalisation) necessitate a systematic approach to elucidating the interplay of networks and spatial organisation. We develop a systems framework towards this end and focus on the effect of spatial localisation of network components revealing its multiple facets: (i) As a key distinct regulator of network behaviour, and an enabler of new network capabilities (ii) As a potent new regulator of pattern formation and self-organisation (iii) As an often hidden factor impacting inference of temporal networks from data (iv) As an engineering tool for rewiring networks and network/circuit design. These insights, transparently arising from the most basic considerations of networks and spatial organisation, have broad relevance in natural and engineered biology and in related areas such as cell-free systems, systems chemistry and bionanotechnology.

P002 Methodological development of single-cell phenotyping and spatial analysis of intestinal leukocyte, stromal and epithelial cell populations in inflammatory bowel disease formalin fixed paraffin embedded tissue by Hyperion imaging mass cytometry

Background Conventional cellular phenotyping of intestinal cell populations by multi-parameter fluorescence cytometry is reliant on collection of fresh tissue for immediate enzymatic or mechanical disaggregation, or cryopreservation of samples. These factors limit widescale use of tissue for research, increase cost, and time for sample collection or preparation. Due to tissue disaggregation suspension cytometry does not provide data regarding spatial localisation of cells in tissue. Formalin fixed paraffin embedded (FFPE) tissue is widely collected at endoscopy and surgery for clinical histopathological assessment, and can be stored and transported at room temperature. Methods We aimed to develop a method for analysing FFPE intestinal tissue using Imaging Mass Cytometry (IMC) in combination with an analysis pipeline for cellular phenotyping and spatial characterisation that preserved multi-parameter, high dimensional phenotyping capabilities normally only afforded by suspension methodologies. FFPE blocks were accessed following written informed consent in accordance with research and ethics committee approval. Carrier-free antibodies specific to cell subsets of interest were selected based on conventional suspension fluorescence cytometry and immunohistochemistry data. Results Antibodies were conjugated to metal isotopes. Antigen retrieval and antibody dilution was optimised on 4µm tissue sections using Tris-EDTA pH 9 initially by immunofluorescence then in multiple assays by Hyperion (Fluidigm) IMC (Figure 1A). An analysis pipeline was developed based on the “Bodenmiller approach” using a combination of R, Python and MATLAB packages: CellProfiler and ilastik to segment single cells, and ImaCyte to explore the resident phenotypes and cellular neighbourhoods in diseased and healthy tissues. A staining panel with 25 antibodies was optimised to identify stromal, epithelial and leukocyte populations. Training algorithms allowed computational segmentation of nuclear, cytoplasmic and non-cellular regions (Figure 1B), cell mask, segmentation and spatial analysis (Figure 1C), and t-SNE (Figure 1D). Representative three parameter images created in MCD viewer (Fluidigm) are shown in Figure 1E to demonstrate cell populations and spatial localisation. Conclusion Quantifiable, multiparameter cellular phenotyping with spatial visualisation can be undertaken with FFPE intestinal tissue using IMC. Due to the existence of archival healthcare samples, the ease of tissue acquisition, processing and storage of FFPE specimens this provides a valuable resource for investigation, including mechanisms of disease pathogenesis, molecular biomarker discovery, and longitudinal pharmacodynamic analysis in clinical trials.

Pandæsim: An Epidemic Spreading Stochastic Simulator

Many methods have been used to model epidemic spreading. They include ordinary differential equation systems for globally homogeneous environments and partial differential equation systems to take into account spatial localisation and inhomogeneity. Stochastic differential equations systems have been used to model the inherent stochasticity of epidemic spreading processes. In our case study, we wanted to model the numbers of individuals in different states of the disease, and their locations in the country. Among the many existing methods we used our own variant of the well known Gillespie stochastic algorithm, along with the sub-volumes method to take into account the spatial localisation. Our algorithm allows us to easily switch from stochastic discrete simulation to continuous deterministic resolution using mean values. We applied our approaches on the study of the Covid-19 epidemic in France. The stochastic discrete version of Pandæsim showed very good correlations between the simulation results and the statistics gathered from hospitals, both on day by day and on global numbers, including the effects of the lockdown. Moreover, we have highlighted interesting differences in behaviour between the continuous and discrete methods that may arise in some particular conditions.

Bayesian transfer in a complex spatial localisation task

Prior knowledge can help observers in various situations. Adults can simultaneously learn two location priors and integrate these with sensory information to locate hidden objects. Importantly, observers weight prior and sensory (likelihood) information differently depending on their respective reliabilities, in line with principles of Bayesian inference. Yet, there is limited evidence that observers actually perform Bayesian inference, rather than a heuristic, such as forming a look-up table. To distinguish these possibilities, we ask whether previously-learnt priors will be immediately integrated with a new, untrained likelihood. If observers use Bayesian principles, they should immediately put less weight on the new, less reliable, likelihood (“Bayesian transfer”). In an initial experiment, observers estimated the position of a hidden target, drawn from one of two distinct distributions, using sensory and prior information. The sensory cue consisted of dots drawn from a Gaussian distribution centred on the true location with either low, medium, or high variance; the latter introduced after block three of five to test for evidence of Bayesian transfer. Observers did not weight the cue (relative to the prior) significantly less in the high compared to medium variance condition, counter to Bayesian predictions. However, when explicitly informed of the different prior variabilities, observers placed less weight on the new high variance likelihood (“Bayesian transfer”), yet substantially diverged from ideal. Much of this divergence can be captured by a model that weights sensory information, according only to internal noise in using the cue. These results emphasise the limits of Bayesian models in complex tasks.

A multiscale ocean data assimilation approach combining spatial and spectral localisation

Ocean data assimilation systems encompass a wide range of scales that are difficult to control simultaneously using partial observation networks. All scales are not observable by all observation systems, which is not easily taken into account in current ocean operational systems. The main reason for this difficulty is that the error covariance matrices are usually assumed to be local (e.g. using a localisation algorithm in ensemble data assimilation systems), so that the large-scale patterns are removed from the error statistics. To better exploit the observational information available for all scales in the assimilation systems of the Copernicus Marine Environment Monitoring Service, we investigate a new method to introduce scale separation in the assimilation scheme. The method is based on a spectral transformation of the assimilation problem and consists in carrying out the analysis with spectral localisation for the large scales and spatial localisation for the residual scales. The target is to improve the observational update of the large-scale components of the signal by an explicit observational constraint applied directly on the large scales and to restrict the use of spatial localisation to the small-scale components of the signal. To evaluate our method, twin experiments are carried out with synthetic altimetry observations (simulating the Jason tracks), assimilated in a 1/4∘ model configuration of the North Atlantic and the Nordic Seas. Results show that the transformation to the spectral domain and the spectral localisation provides consistent ensemble estimates of the state of the system (in the spectral domain or after backward transformation to the spatial domain). Combined with spatial localisation for the residual scales, the new scheme is able to provide a reliable ensemble update for all scales, with improved accuracy for the large scale; and the performance of the system can be checked explicitly and separately for all scales in the assimilation system.

Entrepreneurial capital of the region: the concept, manifestations and spatial localisation

An important element of the explanation why an entrepreneur carries out high-risk transactions is the evaluation and analysis of her or his inner qualities. Thus, there is a need to identify the connection between entrepreneurial risk and capital. At the regional level, there is an ongoing academic discussion as to who the carrier of entrepreneurial capital is and how this capital can be measured and evaluated in view of its direct influence on the business environment and economic growth opportunities of a certain territory. This article presents the findings of a study into the complex structure of the concept of regional entrepreneurial capital and establishes how this concept is connected with such categories as entrepreneurial spirit, entrepreneurial substance, and entrepreneurial ability. Using an estimate of the number of economic entities (individual entrepreneurs and farmers; small, medium, and large enterprises) per 1,000 population, the study demonstrates cross-regional differences in entrepreneurial activity as a manifestation of entrepreneurial capital, including those in the Northwestern Federal District.