Semi-automated approaches for interrogating spatial heterogeneity of tissue samples

Tissues are spatially orchestrated ecosystems composed of heterogeneous cell populations and non-cellular elements. Tissue components' interactions shape the biological processes that govern homeostasis and disease, thus comprehensive insights into tissues' composition are crucial for understanding their biology. Recently, advancements in the spatial biology field enabled the in-depth analyses of tissue architecture at single-cell resolution, while preserving the structural context. The increasing number of biomarkers analyzed, together with whole tissue imaging, generate datasets approaching several hundreds of gigabytes in size, which are rich sources of valuable knowledge but require investments in infrastructure and resources for extracting quantitative information. The analysis of multiplex whole-tissue images requires extensive training and experience in data analysis. Here, we showcase how a set of open-source tools can allow semi-automated image data extraction to study the spatial composition of tissues with a focus on tumor microenvironment (TME). With the use of Lunaphore COMET platform, we interrogated lung cancer specimens where we examined the expression of 20 biomarkers. Subsequently, the tissue composition was interrogated using an in-house optimized nuclei detection algorithm followed by a newly developed image artifact exclusion approach. Thereafter, the data was processed using several publicly available tools, highlighting the compatibility of COMET-derived data with currently available image analysis frameworks. In summary, we showcased an innovative semi-automated workflow that highlights the ease of adoption of multiplex imaging to explore TME composition at single-cell resolution using a simple slide in, data out approach. Our workflow is easily transferrable to various cohorts of specimens to provide a toolset for spatial cellular dissection of the tissue composition.

Fully automated sequential immunofluorescence (seqIF) for hyperplex spatial proteomics

Tissues are complex environments where different cell types are in constant interaction with each other and with non-cellular components. Preserving the spatial context during proteomics analyses of tissue samples has become an important objective for different applications, one of the most important being the investigation of the tumor microenvironment. Here, we describe a multiplexed protein biomarker detection method on the COMET instrument, coined sequential ImmunoFluorescence (seqIF). The fully automated method uses successive applications of antibody incubation and elution, and in-situ imaging enabled by an integrated microscope and a microfluidic chip that provides optimized optical access to the sample. We show seqIF data on different sample types such as tumor and healthy tissue, including 40-plex on a single tissue section that is obtained in less than 24 h, using off-the-shelf antibodies. We also present extensive characterization of the developed method, including elution efficiency, epitope stability, repeatability and reproducibility, signal uniformity, and dynamic range, in addition to marker and panel optimization strategies. The streamlined workflow using off-the-shelf antibodies, data quality enabling downstream analysis, and ease of reaching hyperplex levels make seqIF suitable for immune-oncology research and other disciplines requiring spatial analysis, paving the way for its adoption in clinical settings.

Abstract 5642: Validation of a novel multiplex immuno-fluorescence panel for the spatial analysis of the tumor microenvironment

IHC counterparts. Subsequently, the panel was transferred on a multi-organ TMA including several tumoral and non-tumoral specimens, showing robust performance across multiple tissue types. The protocol was optimized to achieve high staining quality for all 13 markers in terms of signal specificity Investigation of the tumor microenvironment (TME) by multiplex immunofluorescence (mIF) has accelerated the understanding of the spatial immune context in tumors. mIF has proven to be a powerful technique for the identification of new potential biomarkers and therapeutical targets. Despite increased application of mIF assays to characterize the TME, state-of-the art protocols remain technically challenging. Manual execution and use of dedicated reagents render them lengthy and costly. Moreover, their reproducibility is often questioned together with their transferability between different tissue types.

Here, we show the development and validation of an Immuno-Oncology (IO) Core Panel of 13 clinically relevant biomarkers to enable spatial analysis of the immune TME on the COMET™ platform across various tissue types. Formalin-fixed paraffin-embedded human tissue sections from tonsil and a 24-cores multi-organ tissue microarray (TMA) were stained using the IO Core Panel from Lunaphore on the COMET™ platform by fully automated sequential immunofluorescence (seqIF™), which consists of cycles of staining, imaging, and elution. The panel allows for simultaneous detection of CD3, CD4, CD8, CD45, FoxP3, PD1, PD-L1, CD11c, CD20, CD56, CD68, aSMA and Ki-67 by indirect immunofluorescence using unlabeled primary antibodies and Alexa Fluor™ Plus secondary antibodies. The 13-plex IO Core panel was initially developed and validated on tonsil as positive control tissue. To compare immunofluorescent (IF) and immunohistochemistry (IHC) staining patterns, the sections retrieved from COMET™ after seqIF™, were stained by a histology facility with standard IHC established for routine pathological diagnosis. All markers demonstrate accurate detection with specific IF staining, comparably to gold-standard, sensitivity, ratio to background and dynamic range. The repeatability and reproducibility of the automated IO Core Panel on the COMET™ platform was verified by day-to-day tests on one platform and tests among multiple platforms, respectively.

Our study demonstrated the robustness of the validated IO Core Panel across multiple tissue types with highly specific and reproducible results. The marker detection with standard indirect immunofluorescence on the COMET™ instrument allows for future, rapid expansion and customization of the panel including additional primary antibodies towards the need of the individual underlying scientific question.

Citation Format: François Rivest, Victor de Gautard, Vytautas Navikas, Saska Brajkovic, Bastian Nicolai. Validation of a novel multiplex immuno-fluorescence panel for the spatial analysis of the tumor microenvironment. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5642.

Abstract 4616: Automated multiplex immunofluorescence enables single cell analysis of tumor stroma

Background: The tumor microenvironment (TME) consists of malignant cells and supporting non-malignant cellular and non-cellular components that form the tumor stroma. The tumor stroma plays an important role in tumor progression and has emerged as a modulator of anti-tumor immunity (Salmon et al., Nat Rev Cancer 2019) and responses to therapy (Hirata and Sahai, Cold Spring Harb Perspect Med 2017). As such, several therapeutic approaches have recently been developed to target stromal cells as anti-cancer treatments (Valkenburg et al., Nat Rev Clin Oncol 2018, Bejarano et al., Cancer Discov 2021). In addition, the composition of the TME has been recognized as a prognostic factor for survival in cancer patients (Pagès et al., Oncogene 2011). Current protein-based approaches to characterize and better understand the cell composition of the tumor stroma face many limitations such as reagent availability and lengthy protocols. In this study, we identified a list of 22 markers to characterize non-tumoral immune cells, fibroblasts and endothelial cells in the TME, in a single tissue slide. We propose an approach that overcomes reagent incompatibility and opens new avenues of research of tumor stroma.

Method: Multiorgan Tumor Microarray (TMA) was interrogated with a sequential immunofluorescence (seqIF™) panel encompassing protein markers enabling characterization of TME. A 22-plex panel was created based on expanding an already established 13-plex panel (CD3, CD4, CD8, CD11c, CD20, CD45, CD56, CD68, aSMA, FoxP3, Ki67, PD1, PD-L1) by adding 9 additional antibodies (CD11b, CD14, CD31, CD47, CK, FAP, LaminB1, SIRPα, Vimentin). Hyperplex immunofluorescent staining was performed using automated staining-imaging COMET™ platform generating ome-tiff images containing 25 layers: DAPI, 2 autofluorescent and 22 marker channels. Postprocessing of images was done with HORIZON™ image analysis software.

Results: We established a panel of 22 markers that can be analyzed simultaneously on a single tissue slide despite limited variability in primary antibody species. Using seqIF™ protocol allowed the study of colocalization and co-expression of markers not compatible to study simultaneously in the traditional immunofluorescence approach. Our hyperplex data revealed distinct composition of the stromal compartment between different tumor types, highlighting high heterogeneity in the tissue composition and stromal architecture.

Conclusion: COMET™ platform enabled studying in detail TME components and highlighted heterogeneity of tumor stroma across different tissue types. SeqIF™ protocol lifted the limitation imposed by same species antibodies and allowed simultaneous interrogation of markers’ expression preserving their spatial relationship.

Citation Format: François Rivest, Victor de Gautard, Vytautas Navikas, Nadine Nelson, Bastian Nicolai, Joanna Kowal, Saska Brajkovic. Automated multiplex immunofluorescence enables single cell analysis of tumor stroma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4616.

Abstract 1716: Mapping the cellular architecture of the tumor microenvironment by integrating hyperplex immunofluorescence and automated image analysis

The tumor microenvironment (TME) is composed of malignant cells and the surrounding healthy counterpart. The precise identification of the TME components is crucial to understanding how this microecosystem remodels during tumorigenesis and responds to treatment in order to identify its vulnerabilities and treatment opportunities (1). In the past decade, significant efforts have been made to describe the TME using RNA-based technologies (2,3). These approaches shed light on the tumor heterogeneity and variable response to treatment. However, RNA-based biomarker expression profiling has limited relevance as it might not always accurately reflect the actual protein levels (4). In addition, the increasing number of protein biomarkers available led to the development of new technologies that allow the analysis of dozens of proteins on a single tissue slide (5).

The COMET™ platform is an automated instrument that allows the detection of up to 40 antigens on a single slide using sequential immunofluorescence staining (6). By integrating multiplex immunofluorescence technology, we profiled the expression of 40 protein biomarkers across a tissue microarray composed of primary lung tumors and their corresponding metastatic lymph nodes. The combination of the hyperplex panel with an automated image and data analysis pipeline based on an unsupervised machine learning clustering algorithm allowed for the identification of several classes of immune cells with preferential accumulation sites. We identified distinct myeloid cells that coexist within the TME but infiltrate to a higher extent either the primary tumor or the metastatic loci. Harnessing the same approach, we also observed a higher frequency of T regulatory cells in the primary tumors. Subsequently, newly identified population frequencies determined by unsupervised clustering was confirmed by a complementary approach of supervised single-cell analysis.

Our data highlights the potential that microfluidics-based multiplex technology brings into the fields of both digital pathology and immuno-oncology, thanks to its single-cell resolution and the simultaneous detection of multiple protein biomarkers. We demonstrate here how the combination of hyperplex images obtained using the COMET™ platform, along with machine learning clustering analysis, results in an easy workflow for analyzing the complex TME and obtaining a single-cell atlas of tissue specimens.

1.Binnewies M, et al. Nat Med 2018; 24(5):541-550.

2.Lau D, et al. Trends Cancer 2019; 5(3):149-156.

3.Vries NL, et al. Front Oncol 2020.

4.Vogel C, Marcotte EM. Nat Rev Gen 2012; 13:227-232.

5.Lewis SM, et al. Nat Methods 2021; 18:997-1012.

6.Migliozzi D, et al. Microsyst Nanoeng 2019; 5:59.

Citation Format: Pedro Machado Almeida, François Rivest, Quentin Juppet, Joanna Kowal, Benjamin Pelz, Marco Cassano, Deniz Eroglu, Diego Dupouy. Mapping the cellular architecture of the tumor microenvironment by integrating hyperplex immunofluorescence and automated image analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1716.

An adaptive drift-diffusion model of interval timing dynamics

Animals readily learn the timing between salient events. They can even adapt their timed responding to rapidly changing intervals, sometimes as quickly as a single trial. Recently, drift-diffusion models-widely used to model response times in decision making-have been extended with new learning rules that allow them to accommodate steady-state interval timing, including scalar timing and timescale invariance. These time-adaptive drift-diffusion models (TDDMs) work by accumulating evidence of elapsing time through their drift rate, thereby encoding the to-be-timed interval. One outstanding challenge for these models lies in the dynamics of interval timing-when the to-be-timed intervals are non-stationary. On these schedules, animals often fail to exhibit strict timescale invariance, as expected by the TDDMs and most other timing models. Here, we introduce a simple extension to these TDDMs, where the response threshold is a linear function of the observed event rate. This new model compares favorably against the basic TDDMs and the multiple-time-scale (MTS) habituation model when evaluated against three published datasets on timing dynamics in pigeons. Our results suggest that the threshold for triggering responding in interval timing changes as a function of recent intervals.

Spiking neurons, dopamine, and plasticity: timing is everything, but concentration also matters

While both dopamine (DA) fluctuations and spike-timing-dependent plasticity (STDP) are known to influence long-term corticostriatal plasticity, little attention has been devoted to the interaction between these two fundamental mechanisms. Here, a theoretical framework is proposed to account for experimental results specifying the role of presynaptic activation, postsynaptic activation, and concentrations of extracellular DA in synaptic plasticity. Our starting point was an explicitly-implemented multiplicative rule linking STDP to Michaelis-Menton equations that models the dynamics of extracellular DA fluctuations. This rule captures a wide range of results on conditions leading to long-term potentiation and depression in simulations that manipulate the frequency of induced corticostriatal stimulation and DA release. A well-documented biphasic function relating DA concentrations to synaptic plasticity emerges naturally from simulations involving a multiplicative rule linking DA and neural activity. This biphasic function is found consistently across different neural coding schemes employed (voltage-based vs. spike-based models). By comparison, an additive rule fails to capture these results. The proposed framework is the first to generate testable predictions on the dual influence of DA concentrations and STDP on long-term plasticity, suggesting a way in which the biphasic influence of DA concentrations can modulate the direction and magnitude of change induced by STDP, and raising the possibility that DA concentrations may inverse the LTP/LTD components of the STDP rule.