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So I represent Concept Life Sciences.
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We are a contract research organisation and we specialise in developing and conducting translational cell based models to support our clients’ drug discovery programmes in various therapeutic areas including immunology, immune oncology, neuroscience and fibrosis.
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My presentation today concerns macrophages.
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The title of my talk is in Advancing Macrophage Targeted Therapeutics Innovations in Discovery, Modelling, and AI Driven Analysis.
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I would like to start with a brief introduction on macrophage biologies and why macrophages constitute interesting therapeutic targets.
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I will then mention some of the challenges we face in translating research finding from mice to humans.
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And finally, I will show you some of the advanced tools we have developed to enhance target discovery and ultimately progress more efficient macrophage based therapies.
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So macrophages are essential type of immune cells.
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They belong to part of the innate immunity system.
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They are known orchestrators of the various type of immune responses.
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They cooperate and impact other cell types of both the immune and non-immune arm.
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They are known for their complexity and plasticity.
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They are found in virtually all type of tissue and organs, and they are highly adapted to tissue they reside to fulfil particular function.
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They play a dual role in both maintaining health and contributing to disease and here are a couple a few examples how dysregulation of different type of macrophages can lead to serious disorders such as neurodegeneration, fibrosis and inflammatory bowel disease.
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Another type of macrophages is found in the tumour microenvironment.
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These macrophages are known as tumour associated macrophages or TAMs and TAMs are extremely prevalent.
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They comprise up to 80% of tumour stroma and are found across all types of tumours and tumour phenotypes, both the cold and the hot tumours.
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They have been subject of intense research in order to understand their role and potentially exploit a cell type to design novel, more efficient anti-tumour therapies.
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There is consensus that macrophages can be fulfilled both anti-tumour and pro-tumour roles.
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So certainly macrophages can have direct impact on tumours leading to tumour cell phagocytosis or they can mediate direct cancer cell apoptosis through the release of different immune mediators.
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And they can also have indirect role priming other type of immune cells such as T cells and NK cells for those cells to exert cytotoxic effect toward tumour cells.
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Unfortunately, in the tumour microenvironment, macrophages are not really doing what they are supposed to be doing and instead of fighting tumour, they promote tumour, and they do so by several mechanisms including promoting cancer proliferation, survival, leading to tissue remodelling and fibrosis as well as contributing to immunosuppression and by that limiting ability of T cells to recognise cancer cells and destroy them.
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There have been several strategies developed to target TAMs.
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They include boosting phagocytosis of TAMs.
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They include depleting TAMs from tumour microenvironment or reducing their migration towards tumour as well as acting on TAMs already in the tumour by skewing them away from their pro-tumour properties to anti-tumour properties.
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TAM targeting strategies show promise in preclinical tumour models. The human results are a little bit less impressive, and it is believed that the challenges in translating interesting preclinical data into human efficacy lies in biological differences between human and mouse macrophages.
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So obviously in this field we constantly need improved models and better markers in order to enhance anti-tumour strategies targeting TAM.
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So this is really our aim in our CRO to continuously develop in vitro human macrophage models in order to more closely recapitulate the patient scenario and by doing so improve the success of anti-cancer therapies.
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So we started working with human macrophages over 10 years ago.
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We've used expertise in a lot of different drug discovery projects that we are performing on behalf of our clients.
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Working with human macrophages is particularly challenging and the difficulties lie in really the nature of the cells as tissue resident cells.
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They are difficult to isolate, difficult to maintain in vitro in sufficient numbers, viability and functionality.
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And we bypass these constraints by exploiting really the biology of macrophages and the fact that most of tissue macrophages arise from monocytes which are easily accessible from PBMCs which we obtained from healthy donors.
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In this way, we obtain macrophages in high quantities and using well established tools.
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So we have established robust isolation methods and cultural methods using specific plastic type.
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We culture monocytes in the presence of two key differentiation factors.
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Growth factors for macrophages GM-CSF and M-CSF and these two type of growth factors lead to a differentiation of two very different type of macrophages called M1-like and M2-like.
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M1-like being pro-inflammatory anti-tumour and M2-like being anti-inflammatory and pro-tumour.
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Here I would like to give you a little bit of glimpse into our macrophage differentiation cultures.
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You can see time lapse videos capture with the IncuCyte live cell imaging system.
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We start with a population of monocytes which you might have seen two days ago.
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They are around small cells, non-adherent.
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And as the time progresses around day 3-4, you can start seeing morphological differences between these two cell populations.
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And when around day 7-8, so at the end of the pronunciation culture, you can see this difference is really being very pronounced.
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So the M1-like macrophages show that well described fried egg phenotype and M2-like macrophages show the kind of extended shape, fibroblastoid shape.
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So our macrophages are not only different morphologically, but they also show striking differences at the gene expression level.
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We performed qPCR analysis on selected genes and here on the left hand side in the forum of the heat map you can see plotted relative gene expression levels between these two groups in comparison to monocytes.
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So as you can see here in the top section M1-like macrophages express high amounts of pre-inflammatory cytokines such as IL-12 and IL-6 and low levels of anti-inflammatory cytokines.
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IL-10, TGF beta.
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Whereas the opposite is true for the M2-like population.
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It's not only the cytokine which are different between these two cell subsets, we can also see differences at cell surface receptors as well as chemokines and enzymes leading to degradation of extracellular matrix.
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At the protein level, our M1 and M2-like macrophages when activated with LPS they release varied levels of pro-and anti-inflammatory cytokines.
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So IL-12 and IL-10 and one like macrophages release a lot of IL-12 but little IL-10 and M2-like macrophages do the opposite.
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Functionally we have shown that M1-like macrophages when co-cultured with activated T cells, they actually boost T cell activation whereas M2-like macrophages do the opposite.
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So they suppress T cell activation.
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So these characteristics really align very well our system.
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So M1-like and M2-like macrophages with the antitumoral and pro-tumoral function. We have used the assays extensively as I mentioned before in different times drug discovery project.
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In one of the assays, which is the TAMs reprogramming assay, the accessibility of different compounds to modulate TAM responses, whether they can skew responses of M2-like macrophages more towards M1-like macrophages based on cytokine secretion data.
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We can also set up a more complex assay system in the co-culture M2-like macrophages with activated T cells and in here we assess ability of compounds to reverse M2-like suppression of T cell responses.
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Here in this example we show that anti-PD-1 treatment within the sculptures can indeed reverse M2 mediated suppression of T cell activation.
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The diversity of macrophages goes beyond M1-like, and M2-like phenotype and it is well established in the literature that macrophages can undergo further polarisation through a variety of factors as well as cytokines, so we wanted to explore this phenomenon-further.
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We subjected our M-CSF differentiate macrophages to 24 hour incubation with interferon gamma, IL-4, IL-1 beta, IL-10 and IL-6 to further obtain more macrophage cell subsets called here M1, M2, A, B, C, and D And these subsets have been shown in the literature to fulfil a certain pathological role.
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So having shown that macrophage subsets show differences at gene expression level, we wanted to go a little bit deeper here and we perform bulk transcriptomics experiment, and our seven macrophage subsets derived from monocytes isolated from three different individual healthy donors.
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In total, the experiment yielded to 21 samples in total.
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So it was fairly large undertaking.
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In brief, we extracted RNA from our macrophage samples and then we created libraries compatible with illuminance sequencing.
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The sequencing was outsourced, and we received raw data as FastQ files, which we then processed internally into BAM files containing aligned reads.
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And we perform some bioinformatic analysis on the obtained data including principal component analysis and differentially expressed gene analysis.
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So first we wanted to know whether our different seven different macrophage subsets are transcriptionally similar or different.
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So for this PCA plot shows here where we displayed the different subsets in different colours and you can see that individual dots, those dots represent different donors.
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As you can see, there is little donor of variability at the transcriptome level.
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In this particular experiment.
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However, we see transcriptional differences between different macrophage subsets.
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So certainly M1-like macrophages as well as M2a, M2b, and M1 macrophages, they are very different how and M2-like macrophages together with M2c and M2d macrophages clustered together indicating that transcriptionally they are similar.
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On the right hand side, I indicated number of differential express genes in comparison to M2-like population.
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As you can see here that we identified around 600 DEGs in M1-like and M1 subset in comparison to M2-like and M2c and M2d subset in comparison to M2-like subset yielded either none or only 5 DEGs.
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So certainly this two last subsets, they displayed some more subtle changes or changes that could not be picked by our analysis due to setup threshold.
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We wanted to leverage the AI and ML technologies to gain deeper biological understanding of our transcriptomics data.
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For this we partnered with Intellomx and use they or rather they use their Intellomx Distiller analysis tool which identifies gene and pathways.
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This analysis identified 5 interactomes shown here for genes on the left hand side and for pathways shown on the right hand side.
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The Venn diagrams created for different macrophage subsets show the overlap and you can tell that certain drivers within this interactomes are uniquely associated with certain types of conditions, whereas other drivers are shared between different treatments.
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We are at the moment the data is very fresh and at the moment we are looking more closely in order to interpret the analysis as well as compared to the differential expressed gene analysis too.
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So in summary, we believe that our cell based model can work together with AI/ML driven technologies.
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We provide a framework in which our transcriptomics data validates our in vitro cell based models, ensuring compatibility with clinical samples and doing obtaining this information through AI/ML driven approaches.
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Those validated cell based models then can be used to experimentally determine whether the target is important indeed and can also facilitate compound screening and subsequent stages of track discovery and development process.
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I would like to acknowledge our fantastic team at Concept Life Sciences who contributed to this work, as well as Professor Graham Ball from Intellomx who performed the AI/ML analysis, and I would like to encourage you to visit us at both 83 to learn about our services.
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We are also happy to, if there is anyone interested in a handful of targets expressing macrophages, we're happy to share this data with you.
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So please come over and you might not only leave the conference enriched with knowledge and new connections, but maybe some interesting macrophage data as well.
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So thank you very much and I'm happy to take your questions.
