# Morphological profiling of T cells predicts clinical response to VLA-4-targeting natalizumab therapy in patients with multiple sclerosis

Beatriz Chaves, Juan Carlo Santos E Silva, Helder Nakaya, Matheus Almeida, Florence Bucciarelli, Claire Lacouture, Guilhèn Prunier, Saniya Kari, Anne Laurence Astier, Marco Medeiros, et al.

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## To cite this version:

Beatriz Chaves, Juan Carlo Santos E Silva, Helder Nakaya, Matheus Almeida, Florence Bucciarelli, et al.. Morphological profiling of T cells predicts clinical response to VLA-4-targeting natalizumab therapy in patients with multiple sclerosis. 2024. inserm-04728349

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# Morphological profiling of T cells predicts clinical response to VLA-4-targeting natalizumab therapy in patients with multiple sclerosis

**Beatriz Chaves**

beatriz.chaves@inserm.fr

INSERM

**Juan Carlo Santos e Silva**

USP https://orcid.org/0000-0002-7327-4294

**Helder Nakaya**

University of Sao Paulo https://orcid.org/0000-0001-5297-9108

**Matheus Almeida**

Fundação Oswaldo Cruz (Fiocruz)

**Florence Bucciarelli**

University of Toulouse

**Claire Lacouture**

Toulouse Institute for Infectious and Inflammatory Diseases (INFINITY)

**Guilhèn Prunier**

Toulouse Institute for Infectious and Inflammatory Diseases (INFINITY)

**Saniya Kari**

Toulouse Institute for Infectious and Inflammatory Diseases (INFINITY) https://orcid.org/0009-0002-5392-0428

**Anne Astier**

INSERM U1043 https://orcid.org/0000-0002-0144-3431

**Marco Medeiros**

Instituto de Tecnologia em Imunobiológicos (Bio-Manguinhos)

**João Hermínio da Silva**

Fundação Oswaldo Cruz (Fiocruz)

**Roland Liblau**

Institut National de la Sant et de la Recherche Médicale Unit 563, Purpan University Hospital https://orcid.org/0000-0001-5477-5475

**Vinícius Cotta-de-Almeida**

Instituto Oswaldo Cruz (IOC)

**Loïc Dupré**
## Article

**Keywords:** Multiple sclerosis, T cells, VLA-4, natalizumab, high-content cell imaging, pretreatment testing of drug efficacy

**Posted Date:** June 12th, 2024

**DOI:** https://doi.org/10.21203/rs.3.rs-4536459/v1

**License:** © Ⓟ This work is licensed under a Creative Commons Attribution 4.0 International License.

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**Additional Declarations:** There is **NO** Competing Interest.
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9 ¹ Toulouse Institute for Infectious and Inflammatory Diseases (INFINITy), INSERM U1291, CNRS UMR5051, Toulouse III Paul Sabatier University, Toulouse, France.

10 ² Structural and Functional Biology of Biopharmaceuticals, Fiocruz-Ceará, Oswaldo Cruz Foundation (Fiocruz), Eusébio, Brazil.

11 ³ National Institute of Science and Technology on Neuroimmunomodulation (INCT-NIM), Oswaldo Cruz Institute, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil.

12 ⁴ Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil.

13 ⁵ Hospital Israelita Albert Einstein, São Paulo, Brazil.

14 ⁶ Department of Neurosciences, Toulouse University Hospitals, Toulouse, France.

15 ⁷ Laboratory of Recombinant Technology, Bio-Manguinhos, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil.

16 ⁸ Department of Immunology, Toulouse University Hospitals, Toulouse, France.

17 ⁹ Laboratory on Thymus Research (LPT), Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil.

18 ¹⁰ Rio de Janeiro Research Network on Neuroinflammation (RENEURIN), Oswaldo Cruz Institute, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil.

19 ¹¹ Department of Dermatology, Medical University of Vienna, Vienna, Austria.

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21 * Equal contribution to the work

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23 Corresponding authors and lead contacts:

24 Loïc Dupré, Toulouse Institute for Infectious and Inflammatory Diseases, Toulouse, France. E-mail: loic.dupre@inserm.fr

25 Vinicius Cotta-de-Almeida, Oswaldo Cruz Institute, Laboratory on Thymus Research, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil. E-mail: vinicius.almeida@fiocruz.br

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36 **Abstract**

Despite the efficacy of natalizumab in Multiple Sclerosis (MS) treatment, approximately 30% of patients do not respond favorably. Individual heterogeneity of T-cell response to VLA-4 natalizumab-mediated blockade may underlie disparities in treatment efficacy. Here, a high-content cell imaging (HCI) pipeline was implemented to profile the *in vitro* effects of natalizumab on VLA-4-stimulated leukocytes from MS patients prior to treatment.

Unsupervised clustering of image data partially discriminated non-responder MS patients based on morphology, F-actin organization, and signaling-related features in CD8⁺ T cells. Treatment response was assessed through a Random Forest approach with predictive performance of 91% for a discovery cohort and 70% for a validation cohort. Unfavorable treatment response was associated with the inefficacy of natalizumab to impair the ability of pretreated CD8⁺ T cells to spread over VCAM-1. Our study unveils that CD8⁺ T cells from individual MS patients display heterogeneous susceptibility to natalizumab in vitro and highlights the potential of HCI-based pretreatment monitoring to assist individualized treatment prescription.

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51 **Keywords**

Multiple sclerosis; T cells; VLA-4; natalizumab; high-content cell imaging; pretreatment

testing of drug efficacy
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# Introduction

Very Late Antigen-4 (VLA-4) is a heterodimeric integrin composed by the α4 and β1 subunits 1. VLA-4 mediates adhesion, transmigration and intra-tissue trafficking of immune cells, including T cells, by interacting with Vascular Cell Adhesion Molecule-1 (VCAM-1) and fibronectin 2. In addition, this integrin is involved in T-cell immune synapse (IS) assembly 3 and activation 4,5. Because of its pivotal role in multiple aspects of T-cell biology, VLA-4 also contributes to mediate dysregulated T-cell responses, such as those underlying inflammatory diseases 6. This integrin has been particularly studied in the context of multiple sclerosis (MS), in which lymphocytes overexpress VLA-4 on their surface 7 facilitating transmigration to the central nervous system (CNS) 8-10. The identification of this mechanism has been the rationale for targeting VLA-4 as a therapeutic approach to treat MS 11,12.

The humanized anti-α4 monoclonal antibody natalizumab, which blocks VLA-4-VCAM-1 interaction, is currently used to treat relapsing-remitting MS (RRMS) patients 13,14 This therapeutic antibody acts primarily by reducing the homing of pathogenic lymphocytes and activated monocytes to the CNS 15. The NEDA end point (no evidence of disease activity) is commonly used to define controlled MS 16. Currently, 30% of RRMS patients treated with natalizumab present evidence of disease activity 17, not satisfactorily responding to the treatment according to the NEDA criteria. VLA-4 expression on Peripheral Blood Mononuclear Cells (PBMCs) 18, serum levels of IgM specific for phosphatidylcholine 19, metalloproteinases 20, low percentage of CD5+ B cells 21, age at prescription and disease duration 22 have been pointed as biological parameters related to natalizumab clinical response, although their robustness as predictive biomarkers remains to be established. To date, we therefore lack a pretreatment test to predict whether a given person with MS might benefit from the therapy. This would be particularly relevant to avoid exposing unresponsive patients to potential risks, such as the JC virus-associated Progressive Multifocal Leukoencephalopathy (PML) 23, and to tailor treatment decision toward other available therapeutic lines. In this context, we reasoned that profiling the *in vitro* response to natalizumab of leukocytes from MS patients might predict treatment response and therefore assist treatment decision.

For this purpose, we opted for a high-content cell imaging (HCI) approach to monitor the *in vitro* effect of natalizumab on PBMCs from persons with MS prior to treatment. This approach allows to extract precise morphological and molecular metrics pertaining to leukocyte adhesion 24. Because of its deep phenotyping power, it is also particularly prone to discriminate subtle
donor-specific traits that may relate to individual health status²⁵ and responsiveness to therapies²⁶. We first implemented the approach on healthy donor PBMCs and then applied it to MS patients' PBMCs, which pointed to CD8⁺ T cells as the cellular subset for which the most pronounced impact of natalizumab exposure could be captured, based on descriptors related to morphology, F-actin and phospho-SLP76. Unsupervised analysis of the HCI data yielded 2 MS patient subgroups with distinct CD8⁺ T-cell responses that partially aligned with the subsequent treatment response. A machine-learning based approach using the features from the image dataset of a discovery cohort and applied to a validation cohort highlighted the predictive power of the herein developed pre-treatment morphological profiling approach.
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# Results

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## High-content cell imaging captures the *in vitro* sensitivity of primary T cells to natalizumab

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Key molecular events underlying the ability of VLA-4 to trigger leukocyte adhesion, spreading and activation are the recruitment of the adaptor protein SLP76 and the remodeling of the actin cytoskeleton ⁴,²⁷,²⁸. Therefore, to profile the *ex vivo* response of T cells to natalizumab, we implemented an HCI approach, which offered the possibility to collect concomitantly features pertaining to cell morphology, SLP76 phosphorylation and F-actin organization. The HCI pipeline was first optimized on frozen PBMC samples from 10 HD that were or not exposed *in vitro* to natalizumab and seeded onto VCAM-1-coated 384-well imaging plates for analyzing T-cell subsets based on a staining for the CD4 and CD8 coreceptors. Automated confocal imaging was conducted with a 40X objective by focusing on the ventral side of the cells where they were interacting with the VCAM-1-coated surface. A set of 5 descriptors was selected *a priori* for the analysis, including number of adherent cells, cell area, width-to-length ratio, phosphorylated SLP76 (pSLP76) and F-actin intensity (Figure 1A). Natalizumab exposure led to a reduction in cell spreading and in the intensities of the pSLP76 and F-actin stainings in both CD4⁺ and CD8⁺ T-cell subsets (Figure 1B,C). The combination of the considered descriptors through Principal Component Analysis (PCA) separated the natalizumab-treated from the non-treated samples along component F1 (Figures 1B,C), indicating a robust effect of natalizumab across the samples. The main descriptors that drove the segregation of treated versus non-treated PBMCs along component F1 were pSLP76 intensity and width-to-length ratio for CD4⁺ cells and cell area for CD8⁺ T cells (Table S1). Adhesion over VCAM-1 of both CD4⁺ and CD8⁺ T-cell subsets was severely reduced upon natalizumab exposure in a consistent way across the tested samples (Figures 1D, E). Natalizumab-induced reduction of cell area was more frequent across the donors in CD8⁺ than CD4⁺ T cells. In contrast, the effects of natalizumab on width-to-length ratio and F-actin intensity were more consistent across the donors in CD4⁺ than CD8⁺ T cells, while pSLP76 intensity was decreased in both T-cell subsets. Combined analysis of cell morphology, F-actin and pSLP76 intensities by automated confocal microscopy is therefore an adapted approach to capture the sensitivity of T cells to *in vitro* natalizumab exposure.

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127 CD8+ T cells from MS patients are highly sensitive to natalizumab exposure

128 We next applied the HCI pipeline to PBMC samples from 40 persons with MS who had received
129 at least one first line of treatment but had not yet been treated with natalizumab at the time of
130 sample collection (BIONAT cohort 29, see Table S2). In addition to CD4+ and CD8+ T-cell
131 subsets, we extended the evaluation of the in vitro effects of natalizumab on CD14+ monocytes
132 and CD19+ B cells due to the contribution of these leukocyte subsets to MS pathogenesis 30.
133 Our analysis aimed at defining which subsets within MS PBMCs would be most affected by
134 natalizumab exposure (Figure 2A). A prominent effect of natalizumab exposure was the global
135 reduction of the spreading of MS PBMCs (Figure 2B), as observed with the HD T cells. When
136 considering the monocyte subset by gating on the CD14+ cells, our method was however not
137 able to clearly distinguish the natalizumab-exposed from the unexposed samples, as shown on
138 the PCA analysis (Figure 2C). A wide dispersion of the samples was observed when considering
139 CD19+ B cell and CD4+ T-cell subsets, with only a partial discrimination of the natalizumab-
140 exposed and unexposed samples (Figures 2D,E). Such discrimination occurred along the F1
141 component and was mainly driven by pSLP76 intensity (Table S3). The HCI pipeline separated
142 natalizumab-exposed and unexposed samples when considering the CD8+ T-cell subset, as
143 shown by the distribution along the F1 component of the PCA plot, also mainly driven by
144 pSLP76 intensity (Figure 2F and Table S3). Therefore, within the tested MS PBMC samples,
145 CD8+ T cells appeared to be the most sensitive cell subset to the effects of natalizumab exposure
146 as captured by our image-based approach. By analyzing the effect of natalizumab on the CD8+
147 T cells of the 40 patients, we observed a decrease in the number of adherent cells, in cell
148 spreading and in cell elongation, as well as a modest decrease in pSLP76 intensity (Figure 2G).
149 As previously observed with the HD PBMCs, F-actin intensity was not reduced in CD8+ T cells
150 from MS patients upon natalizumab exposure. These findings demonstrate the robustness of the
151 HCI method to capture sensitivity of MS patients’ PBMCs to natalizumab. Furthermore, it
152 points to CD8+ T cells as the cellular subset exhibiting the most pronounced response to in vitro
153 natalizumab exposure.
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155 Unsupervised clustering of HCI data discriminates non-responder MS patients

156 Based on the reasoning that *in vitro* natalizumab effects on T cells prior to treatment might
157 mirror the *in vivo* treatment efficacy, we next tested whether our HCI pipeline could detect
158 differences in pretreatment PBMC samples from patients that had responded favorably or not
to natalizumab treatment. For that purpose, we selected 40 patients from the BIONAT cohort, corresponding to 20 responders and 20 non-responders to natalizumab therapy, based on the NEDA-3 criteria (Table S2). Comparable levels of CD49d were expressed on the different PBMC subsets from the 2 groups of patients, implying that VLA-4 expression does not by itself determine the response to therapy (Figure S1). We applied our HCI pipeline to the pretreatment PBMC samples, focusing on CD8+ T cells since they were identified in the optimization step as the most responsive subset. We increased the sensitivity of the HCI pipeline by imaging the samples with a 63X objective and harvesting an enriched set of 407 features based on intensity, cell morphology (symmetry, threshold compactness, axial and radial distributions) and texture elements (spots, edges, and ridges) of F-actin and pSLP76. We calculated, for each feature, the difference in absolute value between the natalizumab-exposed and the natalizumab-unexposed samples (mean across cells and wells), which was normalized (relative delta) to investigate the effect of natalizumab on multiple features on the same scale (Figure S2). The unsupervised PCA analysis segregated partially the non-responders' samples along the F1 component, with 18 of the 20 samples associated with negative F1 values (Figure 3B). T cells from a typical non-responder patient displayed a reduced spreading upon natalizumab exposure while maintaining a partially polarized shape (Figure 3B, top left image). T cells from the most divergent non-responder patient exhibited increased cell width upon natalizumab exposure (Figure 3B, bottom left image). Samples from responder patients occupied a rather dispersed distribution on the PCA plot. Representative T cells from responder patients displayed a pronounced reduction of cell spreading and an apparent loss of cell polarity upon natalizumab exposure (Figure 3B, top right and bottom right). This analysis suggests that CD8+ T cells from the non-responder patients were less sensible to *in vitro* natalizumab exposure in terms of cell morphology alterations. The discrimination of non-responder samples was further confirmed by a complementary unsupervised K-means clustering analysis built from the 407 features that identified 2 clusters, including one that gathered 18 of the 20 samples from the non-responder patients (Figure 3C). In agreement with the PCA analysis, the samples from the responder patients distributed among the two clusters. Parallel PCA and K-means analysis on the CD4+ T-cell compartment failed to indicate segregation (Figures S4A, B). We further applied a Ward clustering approach, built from the 50 most important features from the PCA (Table S4), which segregated the samples in two main branches, with one gathering 15 of the 20 samples from the non-responder patients (Figure 3D). This branch was characterized by a majority of features, which values were poorly impacted by natalizumab exposure, as opposed to the branch composed of a majority of responder samples. These features included Axial small length, Cell
and Nucleus area, Cell width and multiple F-actin and p-SLP76 texture related descriptors (Figure 3D, Table S4). On the other hand, a minority of discriminative features had values that were highly reduced by natalizumab exposure in the non-responder branch, as Actin symmetry 03 SER-spot, that can be interpreted as actin dots uniformly distributed in three parts of the considered cells. Analysis of Actin axial small length (Figure 3E), which was reduced by natalizumab in cells from responders, and Actin profile 1/5 SER-saddles (Figure 3F), which was impacted by natalizumab in cells from non-responders, illustrates how the cells from the two patient groups may distinctly remodel their F-actin cytoskeleton upon natalizumab exposure. Together, this unsupervised morphological analysis indicates that CD8⁺ T cells from non-responder patients tend to display reduced loss of cell spreading and polarity upon *in vitro* exposure to natalizumab.

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## Prediction of natalizumab treatment outcome with a machine learning model

As the unsupervised analysis indicated a partial discrimination of responder and non-responder patients based on how natalizumab impacted CD8⁺ T-cell features, we implemented a machine learning approach to investigate whether samples from responder and non-responder patients could be distinguished. We opted for a Random Forest (RF)-based approach that trained individual models with the features from the image dataset generated from the 40 MS patient samples, labeled as discovery cohort (Figure 4A). Redundant features displaying >90% correlation were first removed, reducing the number of features to 133. Based on 1024 different combinations used to train the estimators, the RF-based approach generated 33 combinations of features that could distinguish responders from non-responders with F1-score and accuracy higher than 90% (Figure 4B). Surpassing the threshold of 70% performance in each of these scores is indicative of an efficient discrimination. Among the ten most important features, five were related to actin segmentation, symmetry and texture, one to pSLP76 distribution symmetry and texture and one to cell morphology (Figures 4C, S4, S5). In addition to the importance, we also analyzed how often each feature appeared in decision tree combinations that lead to a F1 score higher than 90%. Except for Actin profile 5/5 SER-hole and pSLP76 symmetry 05 SER-dark, all features had frequencies above 50% (Figure 4C), suggesting that their combined integration was key for discriminating samples. To challenge the robustness of the HCI approach to predict treatment response, it was applied to a validation group of 28 patients, denominated validation cohort, composed of 14 responders and 14 non-responders (Table S5).
We tested the same 1024 combinations of features to evaluate their performance on the validation cohort using the 10 most important features as identified through the analysis of the discovery cohort. The most accurate combinations were associated with a F1 score of 77% and an accuracy of 66% (Figure 4D). The two features with low frequencies in combinations in the discovery cohort (Actin profile 5/5 SER-hole and pSLP76 symmetry 05 SER-dark) also displayed low frequencies in the validation cohort (Figure 4E). To further investigate how those ten features were discriminating the two groups of patients, we combined the two cohorts and evaluated, for each feature, the p value pertaining to the comparison of the effect of natalizumab on cells from responders and non-responders (Figure 4F). Actin profile 2/5 SER-saddle and Nucleus area were the two features with p<0.05 that also had consistent importance and frequency among the two cohorts, suggesting that those two features were the most discriminative ones. The impact of natalizumab on the reduction of nucleus area was barely detected in most of the non-responder patients (Figure 4G). This may reflect the propensity of natalizumab to impair T-cell spreading on VCAM-1. On the other hand, natalizumab exposure resulted in a decrease of the Actin profile 2/5 SER-saddle feature in most of the non-responder samples but not in the responder samples (Figure 4H). This is suggestive of a specific remodeling of the actin cytoskeleton (smoother distribution of F-actin in the cellular compartment corresponding to the cytoplasm, see Figure S5), upon natalizumab exposure in the CD8+ T cells from the non-responders. The combination of our HCI-based analysis with a supervised model therefore provides a proof-of-concept that patient-specific responses to natalizumab can be captured *in vitro* prior to treatment in a way to predict the *in vivo* treatment response.

**CD8+ T cells from non-responder patients tend to resist the impact of VLA-4 blockade on cell spreading**

To gain insight into the biological underpinnings of the diverging therapeutic responses, we sought to further explore the meaning of the individual discriminative features. We combined the data from the two cohorts, ranked features according to the p value of the responder/non-responder comparison and completed our analysis of T-cell response to natalizumab by live cell imaging (Figure 5A). Among the 407 features, 29 presented a p value<0.05 (Table S6). For each feature, we calculated and normalized the effect of natalizumab for the responder and non-responder subgroups (relative Δ) and then computed the difference of relative Δ between the 2
subgroups (Figure 5B, X axis). This analysis clearly segregated features into those associated
with a more pronounced effect of natalizumab on T cells from responder patients (Figure 5B,
right side of the chart) and those associated with a more pronounced effect of natalizumab on
T cells from non-responder patients (Figure 5B, left side of the chart). Morphology features
(cell and nucleus area, cell width) were exclusively found in the first set of features, confirming
the notion that T cells from non-responder patients are more resistant to natalizumab-induced
reduction of cell spreading and elongation (see Figure 5C-D for individual patient values for
cell area and cell width). On the other hand, features related to actin texture and distribution
were almost exclusively found in the second set of features. This highlights that CD8+ T cells
from non-responder patients tend to be less susceptible to natalizumab by resisting the impact
of VLA-4 blockade on cell spreading and polarity, but also by adopting a specific actin
cytoskeleton organization. The nature of the actin-related features (e.g. actin ridges and valleys)
implies that natalizumab evoked a more homogenous distribution of F-actin in T cells from
non-responder patients, both at the cell periphery and towards the center of the adhesion plane
(Figure 5E,F). The biased organization of F-actin in T cells from non-responders was not
associated to a difference in F-actin levels (Figure S6). We next asked how the morphological
and actin remodeling effects of natalizumab on T cells from responder and non-responder
patients may translate into their ability to migrate over VCAM-1. For that purpose, we evaluated
morphology, velocity, and straightness of CD8+ T cells from four patients from each treatment
response subgroup, upon interaction with VCAM-1 in an under-agarose setting to prevent non-
specific cell displacements. T cells tended to form homotopic aggregates upon natalizumab
exposure (Figure 5G), as previously reported for other anti-VLA-4 antibodies ¹⁹,³⁰, but this
phenomenon appeared to impact comparably T cells from both subgroups (Figure 5H). Analysis
of cell area confirmed that T cells from non-responders tend to be resistant to the reduction of
cell spreading evoked by natalizumab (Figure 5I). Interestingly, tracking analysis indicated that
intrinsic velocity and ability to migrate along straight paths, rather than that response to natalizumab
*per se*, were the parameters that distinguished T cells from the considered responders and non-
responders (Figures 5J, K). This complementary live microscopy assay therefore suggests that
intrinsic differences in the propensity to migrate over VCAM-1 might differentially predispose
T cells to the activity of natalizumab. Collectively, our analysis points to a multi-parametric
origin of the variable sensitivity of MS patient CD8+ T cells to natalizumab, including intrinsic
determinants related to cell motility, resistance to natalizumab in terms of cell spreading
inhibition and actin remodeling upon natalizumab exposure.
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# Discussion

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Although natalizumab has revolutionized the treatment of relapsing-remitting MS, a substantial proportion of treated patients experience disease relapse. The incomplete response rate (30%) to natalizumab treatment at one year, and even more thereafter ¹⁷, carries a high societal and human burden to health institutions and to the patients and their families. In this context, strategies to predict natalizumab efficacy would allow for the implementation of personalized treatment schemes. Multiple parameters have been proposed to account for treatment failure in individual patients. Given the multiplicity of cellular subsets expressing α4 integrin subunit and the multiplicity of cellular activities that VLA-4 blockade through natalizumab may alter, identifying a biomarker to discriminate and eventually predict individual patient response has remained challenging. Herein, we designed a high-content image-based approach applied to human PBMCs to monitor, through multiple morphological and molecular parameters, the sensitivity of patient cells to natalizumab upon *ex vivo* exposure. We further tested whether such *ex vivo* challenge would capture inter-individual variability that might align with the clinical outcomes of individual MS patients treated with natalizumab.

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In the context of personalized medicine, HCI is emerging as a unique approach that captures simultaneously molecular information and cellular morphometrics with single cell resolution. The application of HCI in drug testing workflows might generate results within a few days ³¹ and deliver treatment efficacy prediction with high prediction accuracy ³². The potential of HCI to provide support for treatment decision has recently been highlighted by its ability to predict drug efficacy in patients with hematological cancers ³³,³⁴. Here, we took the challenge of testing whether HCI might be applicable to screen the efficacy of a therapeutic antibody on non-neoplastic immune cells in the context of an auto-immune disease. Differently from previous HCI studies that monitored tumor cell viability and global activity of surrounding immune cells, our approach was tailored to assess the specific activity of the VLA-4-targeting antibody natalizumab, by stimulating PBMC from persons with MS on VCAM-1 and exposing them to natalizumab. We opted for high-resolution confocal acquisition, aiming to precisely capture individual cell morphological features, as well as parameters related to F-actin organization and SLP76 distribution, given the central position of these molecular activities in the response of leukocytes to VLA-4 engagement. Instead of extracting predefined sets of parameters, we applied clustering and machine learning methods to hundreds of parameters to take full advantage of the rich image datasets.
We provide evidence that such approach focused on the mechanism of action of natalizumab presents with several advantages compared to what has been achieved so far in terms of natalizumab response prediction: timing of prediction, cohort size, statistic strength and biological explanation. Several studies have aimed at predicting the efficacy of natalizumab treatment. High anti-phosphatidylcholine IgM levels in the serum were associated to treatment response in a study involving 33 patients <sup>19</sup>. Decreased levels of B cells and intrathecal immunoglobulin synthesis in the cerebrospinal fluid were also associated with optimal response through the evaluation of 23 patients under treatment <sup>21</sup>. Another study involving 30 patients under natalizumab treatment pointed to a decrease of metalloproteinases serum levels in patients with no relapse episodes <sup>20</sup>. Two studies performed with 29 and 49 patients under treatment pointed to CD49d expression in leukocytes as a biomarker for determining optimal dose and predicting treatment efficacy <sup>35 18</sup>. Our analysis conducted on PBMC samples prior to treatment however failed to establish a link between CD49d expression and natalizumab treatment efficacy, potentially reflecting that the profile of CD49d expression might be altered during the treatment. Finally, age and disease duration at treatment initiation were identified as parameters affecting treatment response in a study of 48 patients <sup>22</sup>. These studies were conducted with classical statistical analysis by evaluating the fitness of individual biological or patient-related parameters. They suffered from not evaluating the robustness of the considered parameters in independent cohorts. Most of them were conducted in the course of treatment and did not evaluate the predictive value of the considered parameters before treatment initiation. In this context, our approach proposes a paradigmatic shift in the search for a treatment efficacy predictor. By combining a miniaturized and automatized image-based bioassay and machine learning approaches, we extract individual patient signatures directly reflecting the quantitative and qualitative impact of natalizumab on primary cells from the patients. It reached a prediction power above 70% and presents predictive values of accuracy and F1-score comparable or above those of other studies that applied machine learning methods to predict clinical outcome in other disease settings <sup>36,37</sup>. We anticipate that our workflow could be generalized to the testing of other therapeutic antibodies across multiple clinical indications.

The HCI pipeline identified CD8+ T cells as the most prominent subpopulation to predict response to natalizumab. The CD8+ T-cell subset was previously suggested as a cell population to predict PML development risk from MS patients <sup>38</sup>. We failed to detect substantial effects of natalizumab exposure on monocytes. This finding might agree with the natalizumab-induced upregulation of α4 on monocytes <sup>39</sup> contrasting with the downregulation of α4 on lymphocytes
¹⁸. Despite the VLA-4 mediated role of B cells in MS neuroinflammation ⁴⁰, our study did not point B cells as a critical population to determine natalizumab effect and predict patient response. The prediction of natalizumab patient clinical outcome by machine learning was mainly driven by a set of actin related features associated to how CD8⁺ T cells adhere, enrich, and distribute actin at the adhesion plane, spread and elongate on VCAM-1. The role of integrins in promoting cell spreading and their implication in many diseases are well established ⁴¹⁻⁴³. The HCI pipeline leads to the identification that non-responder patients are resistant to natalizumab-induced decrease on cell spreading and elongation, which are morphological adaptations needed to allow T-cell interstitial migration through the adaptation and formation of motility related structures such as the lamellipodia and the uropod ⁴⁴. Our data point to an increased migratory profile of the CD8⁺ T cells from the non-responder patients. Thus, we hypothesize that by preserving cell spreading ability, non-responder CD8⁺ T cells have an increased capacity to transmigrate towards the CNS and to establish IS with target cells leading to cell killing and consequent inflammation in the CNS.

Our study showcases how HCI combined to machine learning algorithms may serve as a treatment testing platform to guide treatment prescription. The less sophisticated measurement of a single key biological marker might sound as a most desirable approach. However, our study clearly indicates that among many considered parameters, none could by itself be discriminative enough to predict treatment outcome. Since automated high-content cell imagers are becoming increasingly available in the technological portfolio of biomedical research centers, the development of standardized bioassays based on this technology might become feasible in a near future.

## Limitations of the study

Despite the potential of our analysis in coupling HCI of patient PBMCs to machine learning algorithms and providing biological interpretation, validation in larger cohorts across multiple clinical centers would be necessary before translation in medical practice. In this study we have not investigated whether a more precise examination of the effects of natalizumab on various T-cell subsets might have revealed selective effects and increased the prediction power of our approach. We have also not tested whether our pipeline might predict the risk of developing serious side-effects such as PML. Finally, to reinforce the relevance of the HCI approach, it will be interesting to study how the distinct morphological and actin remodeling effects of
387 natalizumab on T cells from responder and non-responder patients may be sustained by distinct
388 molecular responses and translate into distinct motility capacities.

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# Methods

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## Materials

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<table><thead><tr><th>Reagent or resource</th><th>Source</th><th>Identifier</th></tr></thead><tbody><tr><td><strong>Antibodies</strong></td><td></td><td></td></tr><tr><td>Natalizumab</td><td>Biogen</td><td>Tysabri</td></tr><tr><td>Recombinant Anti-SLP76 (phospho Y145) antibody [EP2853Y]</td><td>Abcam</td><td>ab75829</td></tr><tr><td>Zombie NIR™ Fixable Viability Kit</td><td>Biolegend</td><td>423105</td></tr><tr><td>Human CD4 Antibody</td><td>R&amp;D Systems</td><td>MAB3791</td></tr><tr><td>Human CD8 alpha Antibody</td><td>R&amp;D Systems</td><td>MAB3803</td></tr><tr><td>CD14 antibody |TUK4</td><td>Biorad</td><td>MCA1568T</td></tr><tr><td>Human CD19 Antibody</td><td>R&amp;D Systems</td><td>MAB4867</td></tr><tr><td>Goat anti-Rabbit IgG (H+L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor™ 647</td><td>ThermoFisher</td><td>A21245</td></tr><tr><td>Goat anti-Mouse IgG1 Cross-Adsorbed Secondary Antibody, Alexa Fluor™ 546</td><td>ThermoFisher</td><td>A21123</td></tr><tr><td>Goat anti-Mouse IgG2a Cross-Adsorbed Secondary Antibody, Alexa Fluor™ 546</td><td>ThermoFisher</td><td>A21133</td></tr><tr><td><strong>Recombinant and purified proteins</strong></td><td></td><td></td></tr><tr><td>Recombinant human IL-2</td><td>Peprotech</td><td>200-02</td></tr><tr><td>Recombinant Human VCAM-1/CD106 Protein</td><td>R&amp;D Systems</td><td>ADP5</td></tr><tr><td><strong>Medium components, buffers, and chemicals</strong></td><td></td><td></td></tr><tr><td>RPMI 1640 Medium with GlutaMAX</td><td>Gibco</td><td>21875-034</td></tr><tr><td>Human serum (AB)</td><td>Institut de Biotechnologies Jacques Boy</td><td>201021334</td></tr><tr><td>Penicillin/streptomycin</td><td>Gibco</td><td>15140-122</td></tr><tr><td>Sodium pyruvate</td><td>Gibco</td><td>11360070</td></tr><tr><td>MEM Non-Essential Amino Acids Solution</td><td>Gibco</td><td>11140050</td></tr><tr><td>HEPES</td><td>Gibco</td><td>15630-056</td></tr><tr><td>Phytohemagglutinin (PHA-P)</td><td>ThermoFisher Scientific</td><td>R30852801</td></tr><tr><td>Permeabilization Buffer (PB)</td><td>eBioscience</td><td>00-8333-56</td></tr><tr><td>Paraformaldehyde (PF)</td><td>ThermoFisher Scientific</td><td>50-980-489</td></tr><tr><td>Alexa Fluor™ 546 Phalloidin</td><td>ThermoFisher</td><td>A21123</td></tr><tr><td>Alexa Fluor™ 488 Phalloidin</td><td>ThermoFisher</td><td>A12379</td></tr><tr><td>DAPI (4',6-Diamidino-2-Phenylindole, Dihydrochloride)</td><td>ThermoFisher</td><td>D1306</td></tr><tr><td>CellTrace™ Calcein Red-Orange, AM</td><td>ThermoFisher</td><td>C34851</td></tr><tr><td>CellTracker™ Green CMFDA Dye</td><td>ThermoFisher</td><td>C2925</td></tr></tbody></table>
<table><tr><td>CD8+ T Cell Isolation Kit, human</td><td>Miltenyi Biotec</td><td>130-096-495</td></tr><tr><td><strong>Plates</strong></td><td></td><td></td></tr><tr><td>384-well plate CellCarrier-384 Ultra</td><td>Perkin Elmer</td><td>6057302</td></tr><tr><td><strong>Equipment</strong></td><td></td><td></td></tr><tr><td>High-content screening system (Opera Phenix)</td><td>Perkin Elmer</td><td>N/A</td></tr><tr><td>BD LSR Fortessa Cytometer</td><td>BD Biosciences</td><td>N/A</td></tr><tr><td>MACsQuant VYB cytometer</td><td>Miltenyi Biotec</td><td>N/A</td></tr><tr><td><strong>Softwares</strong></td><td></td><td></td></tr><tr><td>Fiji ImageJ software</td><td>NIH</td><td>N/A</td></tr><tr><td>TrackMate tool in ImageJ</td><td>N/A</td><td>N/A</td></tr><tr><td>Harmony software</td><td>Perkin Elmer</td><td>N/A</td></tr><tr><td>GraphPad Prism v8.0.2</td><td>N/A</td><td>N/A</td></tr><tr><td>XLSTAT Basic 2022.5.1</td><td>N/A</td><td>N/A</td></tr><tr><td>Scikit 1.2.2</td><td>N/A</td><td>N/A</td></tr><tr><td>FlowJo 10.8.1</td><td>N/A</td><td>N/A</td></tr></table>

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## 393 Experimental model and subjects' details

PBMCs from healthy donors (HD): frozen PBMCs from 10 HD were obtained from The Etablissement Français du Sang. The corresponding blood samples were collected following standard ethical procedures (Helsinki protocol) and as per French Bioethics law. The work on PBMCs conducted in this study was covered by an approval by the local ethics committee (Comité de Protection des Personnes Sud-Ouest II et Outre-Mer II). PBMCs were thawed in RPMI-based growth media (RPMI media (Gibco), 5% human serum (heat inactivated, AB positive blood type, Institut de Biotechnologies Jacques Boy), 100 U/ml penicillin/streptomycin (Gibco), 1 mM sodium pyruvate (Gibco), 1X MEM non-essential amino acids (Sigma), 10 mM HEPES (Gibco). Cell culture was maintained at 37°C and 5% CO₂. Cellular composition was analyzed by measuring CD3, CD49d, CD4 and CD8 expression by flow cytometry.

PBMCs from persons with MS: frozen PBMCs from 68 MS patients were obtained from the BIONAT cohort [26]. Patient samples were collected in accordance with law n° 2004-806 of August 9, 2004 relating to public health policy. This research received a favorable opinion from the Committee for the Protection of Persons (CPP) Sud Ouest et Outre Mer II on 06/02/2009, received authorization from the competent authority on 09/02/2009, and was declared to the Commission Nationale Informatique et Libertés (CNIL). The registration number at ClinicalTrials.gov is NCT00942214. The herein studied blood samples were collected prior to natalizumab treatment, from which PBMCs were prepared and frozen. Samples were thawed in RPMI media 5% human serum maintained for 16 h at 37 °C and 5% CO₂ before being processed through the HCI workflow. Details for each patient are available in Tables S2 and S4. Cellular composition was analyzed by measuring CD3, CD49d, CD4 and CD8 expression by flow cytometry.

NEDA-3 classification: classification into responder or non-responder patients was performed according to the NEDA-3 score⁴⁵. No Evidence of Disease Activity-3 is calculated based on the absence of clinical relapses, confirmed disability progression, and new or enlarging lesions on brain magnetic resonance imaging. If a person with MS under treatment has experienced no clinical relapse, no confirmed disability worsening and no new lesions during 2 years, he/she is considered as responder to natalizumab therapy.
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CD8+ T cells from MS patients: From the obtained PBMC, cells were kept in culture for 5 days in the presence of IL-2 and PHA. CD8+ T cells were then purified using CD8+ T Cell Isolation Kit.

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## High-content cell imaging of PBMCs from HD

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**Staining:** A high-content screening 384-well imaging plate (CellCarrier-384 Ultra, Perkin Elmer) was coated overnight at 4°C with VCAM-1 (R&D Systems) at 0.625 µg/mL. The plate was then washed with phosphate-buffered saline (PBS) and cell culture medium. Freshly thawed PBMCs from 10 HD were centrifuged at 430 g for 5 min, split in two and natalizumab (Biogen) was added in part of the cells at the final concentration of 2 µg/mL. Cells were incubated for 30 min at 37°C and 5% CO₂. Next, cells were washed to remove antibody excess. A total of 9000 cells/30 µL were seeded per well of the 384-well precoated plate, followed by a 10 s centrifugation. After stimulation for 10 min at 37°C, non-adherent cells were removed by flipping the plate. Subsequently, the cells were fixed using 3% paraformaldehyde (PFA), washed with PBS and permeabilization buffer (PB, eBioscience), and then stained with Alexa Fluor™ 488 Phalloidin (ThermoFisher), anti-pSLP76 antibody and anti-CD4 or anti-CD8 antibodies (R&D systems) The plate was incubated overnight at 4°C. The plate was then washed with PB and stained with anti-Rabbit Alexa647 (ThermoFisher), anti-mouse IgG2a Alexa 546 antibodies and 4',6-Diamidino-2-Phenylindole, Dihydrochloride (DAPI, ThermoFisher). After 1 h incubation, the plate was washed three times with PBS and subjected to automated confocal imaging.

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**Acquisition:** Images were acquired with a high-content screening system (Opera Phenix, PerkinElmer) equipped with a 40x 1.1 NA Plan Apochromat water immersion objective, 4 sCMOS cameras (2160x2160 pixels, 16 bits, 6.5 µm pixel) and a Yokogawa spinning disk confocal unit. For each well, 49 fields-of-view and 4 Z-planes per field (with step of 0.5 µm, starting at the cell contact plane with the substrate) were acquired. Z-stacks of images were projected along Z-axis using maximum intensity global projection and flatfield correction using Harmony software. Z-projected data were then processed with the same software. First, nuclei were located via DAPI staining using thresholding on the staining intensity. The cell cytoplasms were identified by thresholding on the 488 Phalloidin staining. CD4+ and CD8+ T cells were identified by thresholding on the CD4 and CD8 stainings. Number of detected cells, cell area, cell width to length, pSLP76 and F-actin mean intensities were extracted separately for the CD4+ and CD8+ T-cell subsets with Harmony software.

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## High-content cell imaging of PBMCs from MS patients

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**Staining:** The HCI workflow described above for HD samples was adapted for MS samples with the following modifications. Imaging plates were coated with 1.25 µg/mL VCAM-1. A total of 27000 cells were seeded in each well and stimulated for 10 min at 37°C, before fixation. Cells were stained overnight at 4°C with α-pSLP76 antibody (Rabbit ab75829), Phalloidin 488 (A12379) and either α-CD4 antibody (IgG2a MAB3791), or α-CD8 antibody (IgG2a MAB3803), or α-CD14 antibody (IgG2a MCA1568T), or α-CD19 antibody (IgG1 MAB4867). The secondary staining step consisted in α-rabbit Alexa 647 (A21245), DAPI and either α-IgG1 Alexa 546 (A21123) or α-IgG2a Alexa 546 (A21133).

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**Acquisition:** Plates were subjected to 2 rounds of confocal imaging with a 20x 0.4 NA Plan Apochromat air objective (25 fields-of view covering the entire well) and a 63x 1.15 NA Plan Apochromat water immersion objective (56 fields-of view). The subpopulations of PBMCs were segregated by thresholding for the staining intensity of the relevant staining (CD4 for CD4⁺ T cells, CD8 for CD8⁺ T cells, CD14 for monocytes and CD19 for B cells). Morphological filters (cell and nucleus area) were added to further distinguish lymphocytes from monocytes. Once the populations were defined, basic morphological and intensity parameters were extracted with Harmony software. For the images acquired with the 63x objective, the analysis was enriched by applying the SER and STAR methods of Harmony on the F-actin and pSLP76 stainings. This resulted in library of 407 features per cell.

## Under agarose migration of CD8⁺ T cells from MS patients

A 384-well plate was coated with VCAM-1 (0.625 µg/mL) at 4°C overnight, then rinsed with PBS before cell culture medium was added to accommodate the cells. To produce a confined environment, a 50 µL layer of 0.5% agarose in RPMI was placed in each well used for the assay. CD8⁺ T cells from 4 responders patients and 4 non-responders patients were collected, centrifuged (10 min 122g) and resuspended in RPMI. Responders' cells were stained with Calcein Red-Orange and non-responders' cells were stained with CellTracker™ Green CMFDA. After incubation at 37°C for 30 minutes and centrifugation, cells were resuspended in RPMI medium containing 5% human serum. Pairs of non-responder/responder cells were formed and mixed gently and adjusted for a concentration of 25,000 cells from each patient per 10 µL. Natalizumab was added in a final concentration of 2 µg/mL and cells were then injected under the agarose layer. The plate was then incubated for 30 minutes at 37°C with 5% CO₂, and the microscope's (Opera Phenix, PerkinElmer) temperature and CO₂ levels were adjusted accordingly. An 8-hour acquisition phase followed, capturing images of the entire well at 1-minute intervals at 10x magnification. The resulting images from the first 2 h were then processed using the cell tracking tool from Harmony software, and validated by TrackMate tool in ImageJ, to extract relevant cell motility parameters, allowing for the accurate quantification of cell migration.

## Statistical information

### Unsupervised analysis for sample discrimination based on natalizumab therapy response

The unsupervised analyses were performed using XLSTAT Basic 2022.5.1. The PCA was done using correlation (Pearson) type, with a maximum of 5 calculated components. The chosen component was based on the highest % to explain system variability. The K-mean clustering was calculated using Euclidian distance dissimilarity index, Determinant (W) clustering criterion with a maximum of 10 clusters. The number of clusters selected to explain the results was based on the highest Silhouette score. To validate the number of clusters pointed by the silhouette scores, hierarchical ascendent classification was also performed. The clustering was considered only if both analyses lead to the same result.
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Machine Learning-based analysis for feature combination in natalizumab therapy response

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A Random Forest Classifier (RFC) was embedded in the Scikit-learn python library v1.2.0 ⁴⁶.
We independently analyzed the 407 features from both the discovery and validation cohorts.
The datasets were split into 70:30 training and test samples for the first (n=28:12) and second (n=19:9) cohorts, while ensuring a stratified distribution of the classes. For each unique combination of features, we trained a separate RFC model. The RFC hyperparameters for each model were tuned using the GridSearchCV function with 10-fold cross-validation. The evaluation focused on combinations with the highest F1-Score. To remove redundancies, we removed features having >90% correlation, leading us to retain 133 features in the first cohort.
We subsetted the second cohort with the most important features selected on the first cohort to train individual models for each combination, subsequently evaluating their performance metrics.

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Statistical analysis and graphical representations

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For all experiments, except for the supervised and unsupervised analysis and the bubble chart, the resulting data were subjected to quantification and statistical analysis using GraphPad Prism v8.0.2. The heatmap (Figure 3D) was generated by SRPLOT server and the bubble chart (Figure 5B) was generated by Visual Paradigm server. Specific details regarding the statistical tests employed, representation of significance, and number of experimental data points are provided in the corresponding figure legends.

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Data availability

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Additional information and requests for resources should be directed to and will be fulfilled by
the lead contact, Loïc Dupré (loic.dupre@inserm.fr)

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## Acknowledgments

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The authors thank Astrid Canivet and David Sagnat from the Organoïd facility of the Institut de Recherche en Santé Digestive (IRSD). This work was supported by Brazilian Federal
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Agency for Support and Evaluation of Graduate Education (CAPES, grant numbers CAPES-PRINT - 88887.571313/2020-00) to B.C., and by research grants from the Brazilian Research Council (CNPq) and the Rio de Janeiro Research Council (FAPERJ) to V.C.-d.-A, the São Paulo State Research Foundation (FAPESP, grant numbers 2018/14933-2 to H.N.; 2019/27139-5 to J.C.S.S.), the French MS society (ARSEP) to R.L. and B.C. and the French National Centre for Scientific Research (CNRS, International Research Project SystAct to L.D.).

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## Authorship information

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B.C. contributed to the design of the research, performed experiments, designed the analytical pipeline, analyzed data and wrote the paper; J.S.S, H.N. and M.A. supervised analysis and built the predictive models; F.B contributed to patient sample management and patient data collection; C.L., G.P. and S.K. performed experiments; A.L.A, M.M. and J.S. participated in scientific discussions; R.L participated in research design and scientific discussions; V.C.-d.-A. and L.D. designed the research, supervised the analysis pipeline and wrote the paper.

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## Declaration of Interests

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The authors declare no competing interests.

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<figure><img src="image_3.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Figure 1. Automated cell imaging of healthy donor peripheral blood T cells upon *in vitro* natalizumab exposure**</figcaption></figure>

(A) Schematic of the HCI pipeline applied to PBMCs from healthy donors. PBMCs are exposed or not to natalizumab, seeded on VCAM-1 coated plates, stained for CD4 or CD8, F-actin and pSLP76 and imaged with an automated confocal microscope. Parameters pertaining to cell adhesion, cell shape, F-actin and pSLP76 are extracted separately from CD4⁺ and CD8⁺ subpopulations for further analysis.
(B and C) Representative 40x confocal images of CD4⁺ (B) and CD8⁺ (C) T cells, exposed or not to natalizumab, seeded on VCAM-1 and stained for F-actin (green), pSLP76 (red) and nuclei (blue). Scale bar: 5 µm. PCA plots indicative of the effect of natalizumab on CD4⁺ (B) and CD8⁺ (C) T cells, based on parameters pertaining to adhesion, morphology, actin and pSLP76 intensity. Dots correspond to individual donors with data stemming from two independent experiments, each with 2 replicates.

(D and E) Values for individual parameters are shown for CD4⁺ T cells (D) and CD8⁺ T cells (E) in unexposed and natalizumab exposed samples. Parameters include number of adherent cells, cell area, width to length ratio, pSLP76 mean intensity and F-actin mean intensity. Dots correspond to individual donors with data stemming from two independent experiments, each with 2 replicates. Paired t test was used for statistical evaluation (ns = p > 0.05; * = p ≤ 0.05; ** = p ≤ 0.01 and *** = p ≤ 0.001).
A

<figure><img src="image_8.png" /></figure>

40 MS patients

<figure><img src="image_9.png" /></figure>

PBMCs

<figure><img src="image_10.png" /></figure>

Natalizumab in
vitro treatment

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

VCAM-1 coated plate

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

Automated cell imaging

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

Identification of target PBMC population -
pipeline validation

B

<figure><img src="image_21.png" /></figure>

Untreated

Nuclei

pSP76

F-actin

C

<figure><img src="image_22.png" /></figure>

CD14+ monocytes

D

<figure><img src="image_23.png" /></figure>

CD19+ B cells

<figure><img src="image_24.png" /></figure>

Natalizumab

Nuclei

F-actin

E

<figure><img src="image_25.png" /></figure>

CD4+ T cells

F

<figure><img src="image_26.png" /></figure>

CD8+ T cells

G

<figure><img src="image_27.png" /></figure>

Natalizumab

<figure><img src="image_28.png" /></figure>

Natalizumab

<figure><img src="image_29.png" /></figure>

Natalizumab

CD8+ T cells

<figure><img src="image_30.png" /></figure>

Natalizumab

<figure><img src="image_31.png" /></figure>

Natalizumab

<caption>Figure 2. CD8+ T cells from MS patients display high sensitivity to natalizumab *in vitro* exposure</caption>

(A) Schematic of the HCI pipeline applied to pre-treatment PBMCs from MS patients. PBMCs are exposed or not to natalizumab, seeded on VCAM-1 coated plates, stained for CD4, CD8, CD14 or CD19, F-actin and pSLP76 and imaged with an automated confocal microscope. Parameters pertaining to cell adhesion, cell shape, F-actin and pSLP76 are extracted separately from CD4+, CD8+, CD14+ and CD19+ subpopulations for further analysis.

(B) Representative 20x confocal images of PBMCs from MS patients unexposed (top) or exposed to natalizumab (bottom), seeded on VCAM-1 and stained for F-actin (green), pSLP76 (red) and nuclei (blue). Scale bar: 20 µm. Individual CD4+ and CD8+ T cells, CD19+ B cells and CD14+ monocytes are indicated with a white line. Image does not represent cells from a same well.
(C-F) PCA plots indicative of the effect of natalizumab on CD14+ monocytes (C), CD19+ B cells (D), CD4+ T cells (E) and CD8+ T cells (F), based on parameters pertaining to adhesion, morphology, actin and pSLP76 intensity. Dots correspond to individual patients with data stemming from three independent experiments, each with 2 replicates.

(G) Values for individual parameters are shown for CD8+ T cells in unexposed and natalizumab exposed samples. Parameters include number of adherent cells, cell area, width to length ratio, pSLP76 mean intensity and F-actin mean intensity. Dots correspond to individual patients with data stemming from three independent experiments, each with 2 replicates. Paired t test was used for statistical evaluation (ns = p > 0.05; ** = p ≤ 0.01 and **** = p ≤ 0.0001).
<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>
**Figure 3. Automated cell imaging discriminates CD8<sup>+</sup> T cells according to natalizumab treatment outcome in MS patients**

(A) Schematic of the HCI pipeline applied to pre-treatment PBMCs from MS patients and aiming at identifying morphological signatures associated with natalizumab treatment outcome. PBMCs are exposed or not to natalizumab, seeded on VCAM-1 coated plates, stained for CD8, F-actin and pSLP76 and imaged at high resolution with an automated confocal microscope. A total of 407 parameters are extracted from each imaged CD8<sup>+</sup> T cell for further analysis.

(B) Representative 63x confocal images of CD8<sup>+</sup> T cells from 4 MS patients exposed or not to natalizumab, seeded on VCAM-1 and stained for F-actin (green), pSLP76 (red) and nuclei (blue). Scale bar (brackets): 45 µm. PCA plot aggregating the data from 407 features pertaining to cell adhesion, morphology, F-actin and pSLP76 intensity, distribution and texture (Harmony STAR morphology and SER texture features). Each feature was analyzed as a normalized delta value ((untreated sample – natalizumab-treated sample) / natalizumab-treated x 100)) to account for the effect of natalizumab. Each dot represents a single MS patient with data stemming from three independent experiments. Clinical response to natalizumab treatment is indicated by the dot color.

(C) K-means clustering analysis of the entire HCI dataset, yielding two clusters that partially segregate responder (blue) or non-responder patients (green). A Chi-square test was used for statistical evaluation (\*\* = p ≤ 0.01).

(D) Ward clustering analysis of the HCI dataset represented as heatmap with dendrogram. The analysis was restricted to the 50 top features along F1 component of the PCA. The upper dendrogram represents the clustering of the 40 patients, indicating partial segregation of responder (blue) or non-responder patients (green). The left dendrogram represents the clustering of the 50 features, according to the intensity and the direction of the effect of natalizumab exposure (blue: natalizumab exposure increases the value of the considered feature; red: natalizumab exposure reduces the value of the considered feature).

(E-F) Relative delta values are shown for Actin axial small length (E) and Actin profile 1/5 SER-saddle (F), two features exemplifying the distinct *in vitro* effect of natalizumab on CD8<sup>+</sup> T cells from responder (blue dots) and non-responder patients (green dots). Bars represent the mean of the considered patient subgroup. An unpaired t-test was used for statistical evaluation (\* = p ≤ 0.05; \*\*\* = p ≤ 0.001). Schematics of Actin axial small length and Actin profile 1/5 SER-saddle are represented on the side of the corresponding graphs.
<figure><img src="image_35.png" /><figcaption>Figure 4. Random Forest approach predicts natalizumab treatment outcome in MS patients</figcaption></figure>

(A) Schematic of the machine learning approach build to predict response to natalizumab treatment on the basis of the analysis of pretreatment PBMC with the HCI pipeline. A RF model is trained on the HCI dataset from a discovery cohort composed of 20 responder and 20 non-responder patients. The elaborated model, which runs with 1024 combinations of a restricted set of 10 features, is then challenged with the HCI dataset from a validation cohort composed of 14 responder and 14 non-responder patients.
(B and D) Highest accuracy and F1-score obtained among 1024 combinations of features applied to decision-tree constructions performed in the RF model. (B) represents RF performance with the discovery cohort (40 MS patients), calculated with 133 features. (D) represents RF performance with the validation cohort (28 MS patients), calculated with the 10 most important features identified in the discovery cohort. Dashed line represents wished threshold of 70% for both scores.

(C and E) Importance of each individual feature and feature frequency on RF combinations with F1 score > 91 % in the RF approach for the discovery cohort (C), importance and frequency on RF combinations with F1 score > 70% for the validation cohort (E).

(F) P values of unpaired t-tests calculated for each of the 10 most important features between the group of responder versus non-responder patients from both cohorts merged (n=68, 34 patients in each subgroup).

(G and H) Relative delta values are shown for Nucleus area (G) and Actin Profile 2/5 SER-saddle (H), two prominent features of the RF model. The relative delta values represent the in vitro effect of natalizumab on CD8+ T cells from responder (blue dots) and non-responder patients (green dots). Bars represent the mean of the considered patient subgroup. An unpaired t-test was used for statistical evaluation (* = p ≤ 0.05; ** = p ≤ 0.01).
A

<figure><img src="image_36.png" /></figure>

Features with *p* < 0.05 between
Responders and Non-
Responders' cells

<figure><img src="image_37.png" /></figure>

Features impacted on
Responders' cells

<figure><img src="image_38.png" /></figure>

Features impacted on
Non-Responders' cells

<figure><img src="image_39.png" /></figure>

How natalizumab distinctly affect
features in the two patient groups

<figure><img src="image_40.png" /></figure>

Live imaging validation

B

P value (R versus NR)

<figure><img src="image_41.png" /></figure>

Relative Δ of responder patients

Feature classification

C

<figure><img src="image_42.png" /></figure>

D

<figure><img src="image_43.png" /></figure>

E

<figure><img src="image_44.png" /></figure>

F

<figure><img src="image_45.png" /></figure>

G

Untreated

Non-responders Responders

<figure><img src="image_46.png" /></figure>

Natalizumab

<figure><img src="image_47.png" /></figure>

H

<figure><img src="image_48.png" /></figure>

I

<figure><img src="image_49.png" /></figure>

Responders

J

<figure><img src="image_50.png" /></figure>

Non-responders

K

<figure><img src="image_51.png" /></figure>

Natalizumab
<caption>**Figure 5. CD8⁺ T cells from non-responder patients are resistant to natalizumab inhibition of cell spreading**</caption>

(A) Schematic of the identification of discriminatory features to natalizumab clinical outcomes based on HCI Imaging pipeline and RF analyzes.

(B) Combined analysis of the 29 features with distinct behavior for the responder versus non-responder patients (n=68), based on a p value <0.05 (unpaired t-tests). The Y axis of the chart represents the calculated p value. The X-axis of the chart represents the difference of relative delta mean (Δ responder – Δ non-responder). The size of the bubbles represents the delta average of each feature in the responder patient subgroup, as indicated in the legend. The color of the bubbles represents feature categories, as indicated in the legend.

(C-F) Cell area (C), cell width (D), Actin symmetry 05 SER-ridge (E) and Actin profile 2/5 SER-valley (F) based on the average relative delta of CD8⁺T cells from responder and non-responder MS patients exposed or not in vitro to natalizumab. Data represent the mean of 3 HCI experiments conducted on 68 MS patients. Bars represent the mean of the considered patient subgroup. Unpaired t-tests were used for statistical evaluation (* = p ≤ 0.05, ** = p ≤ 0.01).

(G) Representative snap shots of migrating CD8⁺ T cells from one responder and one non-responder MS patient under agarose at the presence or VCAM-1. Blue indicates non-responders’ cells and red indicates responders’ cells. Scale bars represent 40 µm. At the right of each image, representative zoom of cells *in vitro* treated or not with natalizumab.

(H) Ratio of responder/non-responder cells per homotypic aggregation cluster. Bar represents the average of 4 responders’ cells and 4 non-responders treated with natalizumab cells after 1h of migration.

(I) Cell area of migrating CD8⁺ T cells from 4 responder MS patients and 4 non-responder MS patients *in vitro* treated or not with natalizumab, under agarose and over VCAM-1. Data were obtained from 301 to 3085 cells per condition and are representative of 3 independent experiments. Bar represents the cell area average of 4 responders’ cells and 4 non-responders’ cells after 1h of migration. Sidak's multiple comparisons test was used for statistical evaluation (* = p ≤ 0.05).

(J and K) Mean speed (J) and cell straightness (K) of migrating CD8⁺ T cells from 4 responders MS patients and 4 non-responders MS patients under agarose over VCAM-1. Data were obtained from 301 to 3085 cells per condition and are representative of 3 independent experiments. Bar represents the cell speed average of 4 responders’ cells and 4 non-responders’ cells along 2h tracks. Sidak's multiple comparisons test was used for statistical evaluation (** = p ≤ 0.01).
**Supplementary Files**

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* ChavesSupplMaterialsubmission20240605NatComms.pdf