49 Highly multiplexed detection of critical immune checkpoints and immune cell subtypes in cancerous FFPE tissues using CODEX

BackgroundThere is growing consensus that spatial biology is the key to unlocking the underlying mechanisms of cancer immunotherapy and to predicting patient outcomes. Indeed, a recent example using the Akoya Phenoptics technology revealed a unique phenotypic signature of CD8/Foxp3 positive cells embedded within the tumor microenvironment of patients that responded favorably to PD-1 checkpoint inhibition.1 In this case, the combination of both multiparameter and spatial readouts was required to correlate significantly with outcome. As the number of treatment options expands and knowledge regarding cell types that contribute to treatment mechanisms improves, so too do the number of markers required to analyze responses that enable discovery of new signatures.MethodsHere, we present data from the analysis of human FFPE cancer tissues using an expanded CODEX antibody catalog targeting a variety of immune, immune checkpoint and transcription factors. CODEX enables the highly multiplexed detection of more than 40 targets within the same tissue sample, with single cell resolution and without degradation of the sample.ResultsOur expanded target list enables detection of key macrophage populations, T and B cell subtypes, granulocytes, dendritic cells, natural killer cells, stromal, tumor and epithelial cells. Additionally, the activation state of these immune and tumor cell types can be measured through detection of key immune checkpoints, including PD-1 and PD-L1. Through the addition of these critical markers, both cells known to contribute to treatment outcome and new biomarker signatures can be identified.ConclusionsContinued expansion of spatial biology discovery capabilities will be critical to continuing to improve patient outcomes and to develop new treatment options of solid tumors using cancer immunotherapies.ReferenceBerry S, Taube J, et al. Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade. Science. 2021; 372: 6547.

EXPRESS: Bayesian Consumer Profiling: How to Estimate Consumer Characteristics from Aggregate Data

Firms use aggregate data from data brokers (e.g., Acxiom, Experian) and external data sources (e.g., Census) to infer the likely characteristics of consumers in a target list and thus better predict consumers’ profiles and needs unobtrusively. We demonstrate that the simple count method most commonly used in this effort relies implicitly on an assumption of conditional independence that fails to hold in many settings of managerial interest. We develop a Bayesian profiling introducing different conditional independence assumptions. We also show how to introduce additional observed covariates into this model. We use simulations to show that in managerially relevant settings, the Bayesian method will outperform the simple count method, often by an order of magnitude. We then compare different conditional independence assumptions in two case studies. The first example estimates customers’ age on the basis of their first names; prediction errors decrease substantially. In the second example, we infer the income, occupation, and education of online visitors of a marketing analytic software company based exclusively on their IP addresses. The face validity of the predictions improves dramatically and reveals an interesting (and more complex) endogenous list-selection mechanism than the one suggested by the simple count method.

Alfnoor: a population study on Ariel's low resolution transmission spectra.

<p>In the next decade, the Ariel Space Telescope will provide the first statistical data set of exoplanet spectra, performing spectroscopic observations of about 1000 exoplanets in the wavelength range 0.5 - 7.8 micron during its Reconnaissance Survey. The Ariel Reconnaissance Survey has been designed specifically to identify planets without molecular features in their atmosphere, and select targets (about 500) for accurate chemical characterisation with higher SNR spectroscopic observations.</p> <p>In this work, we investigate the information content of Ariel's Reconnaissance Survey low resolution transmission spectra. We produce different planetary populations using the Ariel candidate target list, randomizing the planetary atmospheres, and simulating the Ariel observations using the Alfnoor software. Then we analyse the dataset, getting three different results:</p> <p>(1) We present a solid strategy that will allow selecting candidate planets to be reobserved in an Ariel's higher resolution, using a chi-squared based metric to identify the flat spectra.</p> <p>(2) Because the reconnaissance survey is not optimised for spectral retrieval, we propose a novel model-independent metric to preliminary classify exoplanets by their atmospheric composition. Without any other planetary information than the spectrum, our metric proves capable of indicating the presence of a molecule when its abundance in the atmosphere is in excess of 10<sup>-4</sup> in mixing ratio.</p> <p>(3) We introduce the possibility of finding other methods to better exploit the data scientific content. We report as an example of possible strategies, a preliminary study involving Deep and Machine Learning algorithms. We show that their performance in identifying the presence of a certain molecule in the spectra is marginally better than our metric for some of these algorithms, while others outperform the metric. </p> <p>We conclude that the the Ariel reconnaissance survey is effective in detecting exoplanets manifesting featureless spectra, and we further show that the data collected in this observing mode have a rich scientific content, allowing for a first chemical classification of the observed targets.</p>

A target list for observing habitable exomoons

<p>There is no confirmed exomoon discovery up to date, and a possible explanation for this is the lower probability of stable moon orbits around close-in planets which are often easier to observe (Barnes & O'Brien 2002). We provide a target list for observations listing known exoplanets which might host habitable moons on stable orbits. For this, we investigate the habitability of hypothethical moons that are on stable orbits around known exoplanets. To determine their habitability, we calculate the incident stellar radiation and the tidal heat flux that might arise in moons depending on their orbital and physical parameters. Our target list contains interesting observation targets which might help in detecting the first habitable exomoon.</p>

Determination of stellar parameters for Ariel targets: a comparison analysis between different spectroscopic methods

Ariel has been selected as the next ESA M4 science mission and it is expected to be launched in 2028. During its 4-year mission, Ariel will observe the atmospheres of a large and diversified population of transiting exoplanets. A key factor for the achievement of the scientific goal of Ariel is the selection strategy for the definition of the input target list. A meaningful choice of the targets requires an accurate knowledge of the planet hosting star properties and this is necessary to be obtained well before the launch. In this work, we present the results of a bench-marking analysis between three different spectroscopic techniques used to determine stellar parameters for a selected number of targets belonging to the Ariel reference sample. We aim to consolidate a method that will be used to homogeneously determine the stellar parameters of the complete Ariel reference sample. Homogeneous, accurate and precise derivation of stellar parameters is crucial for characterising exoplanet-host stars and in turn is a key factor for the accuracy of the planet properties.

Avian Immunome DB: an example of a user-friendly interface for extracting genetic information

Background Genomic and genetic studies often require a target list of genes before conducting any hypothesis testing or experimental verification. With the ever-growing number of sequenced genomes and a variety of different annotation strategies, comes the potential for ambiguous gene symbols, making it cumbersome to capture the “correct” set of genes. In this article, we present and describe the Avian Immunome DB (Avimm) for easy gene property extraction as exemplified by avian immune genes. The avian immune system is characterised by a cascade of complex biological processes underlaid by more than 1000 different genes. It is a vital trait to study particularly in birds considering that they are a significant driver in spreading zoonotic diseases. With the completion of phase II of the B10K (“Bird 10,000 Genomes”) consortium’s whole-genome sequencing effort, we have included 363 annotated bird genomes in addition to other publicly available bird genome data which serve as a valuable foundation for Avimm. Construction and content A relational database with avian immune gene evidence from Gene Ontology, Ensembl, UniProt and the B10K consortium has been designed and set up. The foundation stone or the “seed” for the initial set of avian immune genes is based on the well-studied model organism chicken (Gallus gallus). Gene annotations, different transcript isoforms, nucleotide sequences and protein information, including amino acid sequences, are included. Ambiguous gene names (symbols) are resolved within the database and linked to their canonical gene symbol. Avimm is supplemented by a command-line interface and a web front-end to query the database. Utility and discussion The internal mapping of unique gene symbol identifiers to canonical gene symbols allows for an ambiguous gene property search. The database is organised within core and feature tables, which makes it straightforward to extend for future purposes. The database design is ready to be applied to other taxa or biological processes. Currently, the database contains 1170 distinct avian immune genes with canonical gene symbols and 612 synonyms across 363 bird species. While the command-line interface readily integrates into bioinformatics pipelines, the intuitive web front-end with download functionality offers sophisticated search functionalities and tracks the origin for each record. Avimm is publicly accessible at https://avimm.ab.mpg.de.