# IntegrAlign: a comprehensive tool for multi-immunofluorescence panel integration through image alignment

Leo Hermet, Leo Laoubi, Martial Scavino, Anne-Claire Doffin, Alexia Gazeu,

Justine Berthet, Berenice Pillat, Stephanie Tissot, Sylvie Rusakiewicz,

Marie-Cécile Michallet, et al.

► To cite this version:

Leo Hermet, Leo Laoubi, Martial Scavino, Anne-Claire Doffin, Alexia Gazeu, et al. IntegrAlign: a comprehensive tool for multi-immunofluorescence panel integration through image alignment. Bioinformatics, 2025, 41 (11), pp.btaf567. 10.1093/bioinformatics/btaf567. hal-05345710

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# IntegrAlign: a comprehensive tool for multi-immunofluorescence panel integration through image alignment

Leo Hermet<sup>1,2,3</sup>, Leo Laoubi<sup>1,2,3 D</sup>, Martial Scavino<sup>1</sup>, Anne-Claire Doffin<sup>1,3</sup>, Alexia Gazeu<sup>1,4</sup>, Justine Berthet<sup>1,2</sup>, Berenice Pillat<sup>3</sup>, Stephanie Tissot<sup>5</sup>, Sylvie Rusakiewicz<sup>5</sup>, Marie-Cécile Michallet<sup>1</sup>, Nathalie Bendriss-Vermare<sup>1</sup>, Jenny Valladeau-Guilemond<sup>1,6</sup>, Jean Hausser<sup>7,8</sup>, Christophe Caux<sup>1,2,3,6</sup>, Margaux Hubert<sup>1,2,* D</sup>

## Abstract

**Motivation:** Tyramide-based multiplex-immunofluorescence (mIF) enables the simultaneous analysis of up to seven protein markers on a whole slide, providing a comprehensive approach to study the tumor microenvironment. Integrating multiple mIF panels through image alignment of serial slide significantly expands the number of cell populations analysed in a single space. IntegrAlign was developed to optimize this integration on serial whole slides, enhancing the value and applicability of -mIF for comprehensive spatial analyses and enabling biomarker discovery at scale.

**Results:** IntegrAlign, leveraging the SimpleITK toolkit, applies a two-step alignment using rigid and B-spline transformations to integrate serial mIF whole slides. Validation on simulated and real datasets demonstrated alignment accuracy below the diameter of a cell nucleus (~6 µm), outperforming existing methods. This precision enhances spatial analyses by combining extended phenotypic data, supporting novel insights into tissue architecture and cellular interactions.

**Availability and implementation:** IntegrAlign is open-source, implemented in Python, and freely available under the MIT license at https://github.com/CAUXlab/IntegrAlign.

## 1 Introduction

Tissue histopathology, in particular hematoxylin and eosin (H&E) staining and immunohistochemistry (IHC), has long allowed visualizing the microscopic structures of tissues and remains central to the diagnosis of many diseases, especially cancer. Traditionally, tissue images are examined by anatomopathologists to evaluate the overall architecture and identify structural abnormalities. To support this goal, machine learning and artificial intelligence (ML/AI) are being developed to automatically extract spatial and architectural information from tissue images, moving towards a computer-aided diagnosis.

Despite these advances, H&E only visualizes tissue architecture and IHC is limited to small number of markers. To address this, highly multiplexed tissue imaging methods [such as CODEX (Goltsev et al. 2018), multiplexed ion beam

imaging (MIBI) (Ptacek et al. 2020), imaging mass cytometry (IMC) (Giesen et al. 2014)] were recently developed to profile many markers and cell populations on a single slide. However, highly multiplexed imaging techniques suffer from high price, long image acquisition time, and small size of analysed areas which require focusing on small regions of interest (ROIs), all of which limits their clinical relevance and make them inapplicable to large cohorts of patients. Multiplex-immunofluorescence (mIF) offers an attractive tradeoff, allowing to analyse multiple markers and populations (up to seven markers stained per slide) on a whole slide for large cohorts in a reasonable time frame and cost.

It is classical to perform mIF on serial slides with complementary panels of markers to obtain a more detailed tissue profiling. However, the most common and simplest way to

<footnote n="1"><sup>1</sup>Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, Univ Lyon, Université Claude Bernard Lyon 1, INSERM U1052, CNRS 5286, Lyon, 69008, France</footnote>

<footnote n="2"><sup>2</sup>Laboratoire d'Immunothérapie des Cancers de Lyon (LICL), Lyon, 69008, France</footnote>

<footnote n="3"><sup>3</sup>Centre Léon Bérard, Lyon, 69008, France</footnote>

<footnote n="4"><sup>4</sup>Multisite Institute of Pathology, Groupement Hospitalier Est du CHU de Lyon, Hôpital Femme-Mère-Enfant, Bron, 69029, France</footnote>

<footnote n="5"><sup>5</sup>Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital, Lausanne, CH-1011, Switzerland</footnote>

<footnote n="6"><sup>6</sup>LabEx DEVweCAN, Lyon, 69008, France</footnote>

<footnote n="7"><sup>7</sup>Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden</footnote>

<footnote n="8"><sup>8</sup>SciLifeLab, Stockholm, 171 65, Sweden</footnote>

<footnote>*Corresponding author. Sanofi, 1541 Avenue Marcel Mérieux, 69280 Marcy L'Etoile, Lyon, France. E-mail: margaux.hubert10@gmail.com.</footnote>

<footnote>Associate Editor: Macha Nikolski</footnote>
interpret the mIF data is an individual analysis of each panel, which reduces the potential for in-depth spatial analysis. To overcome this limitation, data from different mIF panels performed on serial slides can be integrated. This requires image alignment, also called registration, to process two different data sets into a common coordinate system: registration estimates a transformation which maps points from one image to the corresponding points in another image. This method has previously been applied to consecutive staining on a same slide generating multiple images (Muhlich et al. 2022, Doyle et al. 2023) and to biomedical images such as MRI brain scans (Miao et al. 2016, Simonovsky et al. 2016, Yang et al. 2017, Cheng et al. 2018). However, the registration of images coming from serial tissue slides is challenging due to several factors related to tissue preparation, including morphological changes (compression or stretching), missing or damaged areas, as well as tissue rotation and translation. While rigid registration methods are defined as geometric transformations that preserve all distances, non-rigid registrations enable transformations with image deformation. Such strategies have previously been applied to serial H&E slides (Rossetti et al. 2017, Jiang et al. 2019, Kiemen et al. 2022), but never using mIF images generated with distinct panels of markers and fluorochromes.

In this article, we introduce IntegrAlign, a novel method for integrating two mIF panels by image alignment using the SimpleITK toolkit (Yaniv et al. 2018). We present the first open-source pipeline developed to augment the analytical capabilities allowed by the mIF technique. Through a comparison with existing algorithms based on manual or automated detection of anchor points, we clearly demonstrate the effectiveness and accuracy of IntegrAlign. By aligning mIF images, integrating multiple panels using IntegrAlign increases the number of cell populations that can be analysed simultaneously at the neighborhood level (Fig. 1A). IntegrAlign will synergize with a fast and inexpensive spatial analysis of whole tissue slides by mIF to open new avenues for the comprehensive analysis of spatial biology and the identification of new biomarkers in large cohorts of patients.

# 2 Materials and methods

## 2.1 Image acquisition and data acquisition

In this section, we describe the different steps required to generate input files for the IntegrAlign pipeline (Fig. 1B). Formalin-fixed paraffin-embedded (FFPE) tumor tissues have been collected at diagnosis from surgical specimens. Written informed consent for the use of samples for research purposes was obtained from all patients prior to analyses. For each tumor (except neuroblastomas), a pair of two 4 µm thick serial sections were stained by Opal-based multiplex-immunofluorescence (mIF) to detect cell types corresponding to the associated panel (Table 1).

To design the mIF panels, specific markers were chosen based on previous literature data (Hubert et al. 2020). Briefly, the seven-color mIF assay was conducted with sequential staining cycles on an automatic stainer. Stained FFPE samples were then scanned and analysed using InForm or IFQuant software (Fig. 1C and D): (i) tissue segmentation was first performed to define tissue and empty/artifact areas, (ii) then cell segmentation was achieved using nucleus recognition and expansion, (iii) and finally cell phenotyping was performed to attribute mutually exclusive phenotypes to each cell.

## 2.2 IntegrAlign pipeline

IntegrAlign pipeline leads to the integration of cell coordinates from two serial slides in the same system using scans alignment. Figure 2 summarizes the different steps of the IntegrAlign pipeline that are detailed in the following sections.

### 2.2.1 Image processing

To achieve the alignment of two images, it is essential to first reduce the complexity of the images, which are originally composed of right high-resolution channels. The DAPI channel, which highlights cell nuclei, was selected because it is the mandatory common marker between serial slides. Also, we chose DAPI among all common markers because it stains every nucleus, providing a complete representation of the tissue structure necessary for aligning consecutive slides. In contrast, tumor-specific markers would omit many cells, leading to poor alignment and inaccurate tissue correspondence. While the full-resolution images exhibit a discrepancy in the spatial arrangement of cells between the two serial slides at high magnification (corresponding cells from serial slides no. 1 and two failing to match in ×60 magnification, Fig. 3A), we observed that the cell density patterns remain consistent at lower magnification (×10, Fig. 3A). Consequently, to eliminate unnecessary single-cell information while preserving the overall density patterns, we used a down-sampled version of the DAPI channel (1/32th of the original resolution), which was encapsulated within the qptiff-formatted image (contains multiple resolutions of the original image for pyramid representation).

This process reduced the image size from eight channels at 48 × 960 × 36 × 480 pixels to one channel at 1530 × 1140 pixels, decreasing computational time while preserving critical information required for accurate alignment.

These downscaled images can be visualized prior to the alignment step to identify and exclude poor-quality slides (e.g., those with excessive tissue tears). This early quality check ensures that only well-preserved slides proceed to the alignment process, thereby improving both computational efficiency and the reliability of the results.

Another key step of the pipeline is the user interface for manual pre-processing. Users can crop out non-analysed or irrelevant tissue regions and adjust the brightness of both slides to improve consistency, as DAPI intensity often varies between serial slides. The interface also allows users to manually align the slides, providing an initial superposition that facilitates alignment (particularly useful when the slides are upside down for example).

### 2.2.2 Alignment

The goal of alignment is to map points from one image to their corresponding points in another image by estimating the transformation of the coordinates. This can be achieved using the SimpleITK toolkit (https://github.com/SimpleITK/SimpleITK) (Yaniv et al. 2018) and in particular three key components: the optimization algorithm, the similarity metric, and the transformation.

#### 2.2.2.1 Similarity metrics

In SimpleITK (Yaniv et al. 2018), alignment relies on the values of pixels intensity in both images. This is particularly relevant in our case, where the serial slides share similar tissue architecture but with different pixel intensity. In order to measure how closely two images resemble each other we used
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Principles of IntegrAlign tool and multiplex-immunofluorescence analysis. (A) Objective of the IntegrAlign tool for multiplex-immunofluorescence analysis. This panel illustrates how IntegrAlign accurately matches cell coordinates (DAPI) between two serial slides, showing the correlation factor between the rasterized cell positions of the two slides before and after alignment. Once alignment is done, all cell types are merged, resulting in a total of 12 distinct cell types in the coordinate system of the target slide. (B) Workflow of multiplex-immunofluorescence analysis from tissue to spatial data. (C) Illustration of a 7-color opal-based multiplex-immunofluorescence staining. (D) Representation of tissue segmentation (D.b), nuclei segmentation (D.c), and phenotyping (D.d) from a scan (D.a) in breast tumor.</figcaption></figure>

<table><caption>**Table 1.** Data overview.</caption><thead><tr><th>Tumor type</th><th>Nb patients</th><th>Cohort</th><th>Panel(s)</th><th>Serial</th><th>Common markers</th><th>Tumor section</th><th>Automated staining</th><th>Slide scanning</th><th>Image analysis software</th><th>File type</th></tr></thead><tbody><tr><td>Neuroblastoma</td><td>4</td><td>CLB</td><td>DC</td><td>NO</td><td>-</td><td>Microtome</td><td>Bond RX</td><td>Vectra Polaris</td><td>InForm</td><td>.qptiff</td></tr><tr><td>Breast</td><td>2</td><td>CLB</td><td>DC</td><td>YES</td><td>All</td><td>Microtome</td><td>Bond RX</td><td>Vectra Polaris</td><td>InForm</td><td>.qptiff</td></tr><tr><td>Renal Cell Carcinoma</td><td>1</td><td>IMMUcan/RCC</td><td>T cells</td><td>YES</td><td>CK, CD8</td><td>Microtome</td><td>Ventana</td><td>PhenoImager</td><td>IFQuant</td><td>.qptiff</td></tr><tr><td>NSCLC<sup>a</sup></td><td>1</td><td>IMMUcan/NSCLC</td><td>T cells</td><td>YES</td><td>CK, CD8</td><td>Microtome</td><td>Ventana</td><td>PhenoImager</td><td>IFQuant</td><td>.qptiff</td></tr><tr><td>Breast</td><td>1</td><td>IMMUcan/BC1</td><td>T cells</td><td>YES</td><td>CK, CD8</td><td>Microtome</td><td>Ventana</td><td>PhenoImager</td><td>IFQuant</td><td>.qptiff</td></tr></tbody></table>

<footnote><sup>a</sup> Non-small cell lung cancer. Panel DC corresponds to seven markers: DAPI, CLEC9A, BDCA2, CLEC10A, CD8, CK (Cytokeratin), DC-LAMP as already described in Hubert et al. (2020). Panel T corresponds to seven markers with two different panel for each slide, (1): PD1, CK, Ki67, GB, CD8, PDL1; (2) CD4, CK, CD3, CD56, CD8, FOXP3.</footnote>
<figure><img src="image_3.png" /><figcaption>**Figure 2.** IntegrAlign pipeline. Overview of the IntegrAlign workflow composed of five main stages. (A) In the first stage, DAPI channel images are downscaled (1/32e) for both serial slides to save computational time and retain only the relevant information. (B) Then, downscaled serial slides are aligned using the rigid and B-spline transformations. (C) Several B-spline transformations are evaluated and optimal mesh size is selected based on the quality of deformation and cell-to-cell matching. Indeed, quality control images showing strength and direction of induced deformations are available for visual checking. Alignment accuracy is verified using the Napari viewer displaying both slides with the cursor transformed from first (cursor) to second slide (red point). (D) After achieving optimal alignment, using the same dual slide Napari viewer, non-alignable tissue sections are excluded by manual clipping. (E) Finally, coordinates of both serial slides are merged by transforming those of slide no. 1 into slide no. 2 and removing the ones in excluded areas.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Figure 3.** Image pre-processing and alignment with the package SimpleTK. (A) DAPI channel images of two serial slides at ×1, ×10, and ×60 zooms for original image (resolution for slide 1 = 48 960 × 36 480 pixels) on the left and downscaled image (1/32th; resolution for slide 1 downscaled = 1530 × 1140 pixels) on the right. Red and orange rectangles indicate the zoomed-in areas before and after downscaling. (B) Representation of the alignment of two downscaled serial images using SimpleTK package including two successive transformations with an optimization algorithm employing pixel-wise correlation metric: (1) Euler2DTransform (rigid transformation) performing translation and rotation, followed by (2) deformation using BSplineTransform (B-spline transformation). Visualization of the moving images at global optimal 1 and 2 are from resampling.</figcaption></figure>
the similarity metric called normalized cross-correlation coefficient which quantifies the similarity of pixel intensities between images after normalization. This metric is defined as:

$$C(f, m) = - \frac{\langle f - \bar{f}, m - \bar{m} \rangle^2}{|f - \bar{f}|^2 |m - \bar{m}|^2}$$

in which, f and m are the vectors of image pixel intensities. The minus sign allows the optimization process to minimize the metric. The correlation is computed efficiently without requiring square root calculations, making it practical for optimization purposes.

This similarity metric is computed between a fraction of pixels from the aligned images. Consequently, to compute the similarity metric at each iteration, we need the corresponding transformed image.

This image is obtained through resampling: a regular grid of points is defined using the pixels of the reference/fixed image. At each iteration, all pixel coordinates from the grid can be transformed (using the corresponding parameters) to the target image with the direct transformation. Then, a linearly interpolated value is computed from the surrounding points in the target image to obtain the corresponding intensity value (pixel's value) that will be imported back (mapped) to the original coordinate (in the fixed image system). This results in a resampled image, named moving image, that can be used with the fixed image to compute the similarity metric at each iteration.

### 2.2.2.2 Optimization algorithm

In order to define the optimal transformation, we used an optimization algorithm called the Limited-memory Broyden-Fletcher-Goldfarb-Shanno with Bound constraints (Byrd et al. 1995) (L-BFGS-B). It corresponds to a gradient-based optimization algorithm commonly used in numerical optimization that belongs to the class of quasi-Newton methods, designed for optimizing smooth, non-linear functions. The global optimum is defined at convergence of the similarity metric, representing the ideal transformation of the fixed image within the moving image's coordinate system to maximize the correlation.

### 2.2.2.3 Transformation

While the similarity metric quantifies the likeness between images, the optimization algorithm needs also a transformation that involves adjusting parameters to dictate how one image aligns with another. In SimpleITK (Yaniv et al. 2018), the transformation is done from the physical virtual space (corresponding to the physical space of the reference image in our case) onto the physical space of the target image.

Here, we employ composite transformations, with the sequential application of two types of transformation. This approach allows to first implement partial alignment and then introduce more complex deformations. Indeed, the first step was to use a rigid transformation (Fig. 3B) which encompasses rotation and translation adjustments. It can be defined as:

$$T(x) = A(x - c) + t + c$$

where A is the rotation matrix, c the fixed center around which the rotation occurs, and t the translation vector. This partial alignment step serves as a foundational stage,

mitigating initial misalignments between the images. Indeed, applied to the optimization algorithm of image registration, it leads to the global optimal 1 (Fig. 3B) which is the result of the best conformation for correlation between fixed and moving images with a rigid transformation.

Then, as a second step, we can start from this optimum to integrate deformations using a B-spline transformation (Fig. 3B). Here, the deformation field is modeled using B-splines, allowing local deformation through a grid of control points. Indeed, the deformation is defined by a sparse grid of control points $\lambda_j$, each influencing a specific deformation $\vec{g}(\lambda_j)$. We can regulate the number of control points and thereby modulate the resolution of the transformation by adjusting the mesh size. Using a cubic spline order (degree of the piecewise polynomial) by default, the mesh size parameter corresponds to the number of polynomial patches comprising the finite domain of support and can be defined as:

$$\text{mesh size} = \text{number of control points} - \text{spline order}$$

Next, using a B-spline interpolation kernel, we applied the deformations defined by the control points $\vec{D}(\vec{x})$ to any point $\vec{x}$ in the image.

Using those two transformations subsequently results in the global optimal 2 (Fig. 3B) with a final conformation corresponding to a maximized correlation between fixed and moving images after translation, rotation, and deformation.

### 2.2.3 Definition of the optimal mesh size

For the B-spline transformation, we can induce different level of deformation depending on the number of control points using the mesh size parameter. We used different strategies described in this section to determine the most suitable mesh size and to provide information about the overlapping images, the induced deformations, as well as the cell-to-cell matching.

#### 2.2.3.1 Visualization of overlapping slides

First, for a comprehensive evaluation of the alignment, IntegrAlign allows the visualization of the overlay between both images—fixed and moving (Fig. 3B). DAPI channel downscaled image from slide no. 2 was resampled using SimpleITK to retrieve the aligned moving image to the fixed image. Then, the pixels of the resampled image of DAPI from slide no. 2 downscaled were combined with the image of DAPI from slide no. 1 downscaled using alpha blending (i.e. linear interpolation of pixel intensities to visualize their superposition).

#### 2.2.3.2 Deformation grid

To determine the most suitable mesh size, we integrated the visualization of the deformations (illustrated in Fig. 5D left) in the IntegrAlign pipeline with a transformed grid overlaying the resampled moving image. In addition, we showed the control points (blue dots) and their associated displacement (black arrows). This visualization helps identifying areas of deformation and their relevance to the needed adjustment of the slide.

#### 2.2.3.3 Jacobian

While the deformation grid provides an overview of how the tissue is being deformed, we included in IntegrAlign a more
intuitive information about the specific locations of these deformations within the tissue. Indeed, using SimpleITK, we computed the Jacobian determinant. Briefly, from the B-spline transformation we generated the corresponding vector field of displacements from which we determine the gradient (rate of change of displacement). Then, we evaluated the determinant of the resulting matrix, denoted as Jacobian:

$$ \text{Jacobian} = \det \left[ I + \frac{\partial u}{\partial x} \right] $$

where I the identity matrix and $\frac{\partial u}{\partial x}$ the gradient of the displacement field. This provided a scalar image where each pixel's value represents the local deformation magnitude (Fig. 5D—right panel). In this case, a determinant greater than 1 indicates compression, signifying that the tissue is being squeezed or compressed at that location. Conversely, a determinant less than 1 corresponds to expansion, indicating that the tissue is being stretched or expanded. This information allows to verify the coherence and accuracy of the deformations, providing insights into the dynamic changes occurring within the tissue.

### 2.2.3.4 Duplicated cursor for cell-to-cell matching

Last step included in IntegrAlign pipeline to achieve an optimal alignment is the evaluation of the precision of the transformation at the single-cell level using Napari (Sofroniew et al. 2025), a multi-dimensional image viewer for python, in full-resolution images. Indeed, a side-by-side display of both images with a duplicated cursor moving over the two images (thanks to the transformation of its coordinates) allow the evaluation of alignment at specific coordinates (Fig. 2C), enabling the visualization of the mapping of each coordinate. This approach allows to thoroughly examine the entire slide, assess the overall alignment, and identify any regions where alignment may be problematic.

### 2.2.4 Image clipping

We also included in this tool the possibility to eliminate regions that differ between the two slides (i.e. tissue tearing or folding, Fig. S1, available as supplementary data at Bioinformatics online). So, filtering of these regions is a crucial step allowed by IntegrAlign pipeline to get a robust integrated dataset after alignment. Using the previously described side-by-side visualization with the duplicated cursor, non-alignable tissue can be located and excluded with manual clipping (Fig. 2D).

### 2.2.5 Cells integration

Finally, cell coordinates of one slide are mapped to the other, with exclusion of cells located in non-alignable tissue sections delimited with the manual clipping. This involves transforming cells coordinates from the fixed (reference) image to the moving (target) image.

For a straightforward qualitative alignment validation, we can also use a common marker (in our case Cytokeratin) to check if the structure aligns correctly between the slides, ensuring accurate alignment.

### 2.2.6 Availability

IntegrAlign, developed in Python, is freely available under the MIT license at https://github.com/CAUXlab/IntegrAlign.

## 2.3 Validation methods

### 2.3.1 Simulation of a serial slide

In order to evaluate the IntegrAlign accuracy (alignment error in µm), we artificially generated a corresponding serial slide from the original. To achieve this, we induced random B-spline deformations (mesh size = 3) with maximum control point displacement to simulate potential inter-slide deformations. Then, to replicate varied spatial orientations, specific rotation and translation transformations were also implemented. Using three maximum displacements of the control points (100, 500, and 1000 µm) in the B-spline transformation, we obtained three different deformed slides and the corresponding ground truth composite transformation.

### 2.3.2 Misalignment distance

In order to quantify the quality of the alignment, we defined corresponding points in two slides and computed the distance between transformed (from slide 1) to ground truth coordinate (slide 2). Local alignment error (µm) was defined as follows:

$$ err_i = \sqrt{(x_{\text{true},i} - x_{T(i)})^2 + (y_{\text{true},i} - y_{T(i)})^2} $$

where $err_i$ represents the misalignment error for the ith point, $x_{T(i)}$ and $y_{T(i)}$ are the transformed coordinates and $x_{\text{true},i}$ and $y_{\text{true},i}$ are the ground truth coordinates.

### 2.3.3 Nearest neighbor method

We computed the distance between every CD8⁺ cells and their nearest tumor cell (CK positive) denoted as $NN_{CD8⁺}(i)$:

$$ NN_{CD8⁺}(i) = \min_{\forall j} \sqrt{(x_i - x'_j)^2 + (y_i - y'_j)^2} $$

where $(x_i, y_i)$ are the coordinates of CD8⁺ cell $i$, $(x'_i, y'_i)$ are the coordinates of CK⁺ cell, and $\min_{\forall j}$ is the minimum over all CK⁺ cells.

### 2.3.4 Confidence interval on median

To quantify the uncertainty around the median of the distances between CD8 and CK cells, we computed its confidence interval (CI) using the bootstrap method. This approach involved generating 1000 resampled datasets by randomly selecting nearest neighbor distances (CD8-CK) with replacement from the original distribution. For each resampled dataset, we computed the median. The 95% confidence interval was then determined by calculating the 2.5th and 97.5th percentiles of the medians obtained from these resampled datasets.

### 2.3.5 Other alignment methods

We compared IntegrAlign efficacy with the following existing methods:

#### 2.3.5.1 Manual

Estimation of a transformation using control point mapping. We utilized the Scikit-image library (van der Walt et al. 2014) to estimate a projective transformation based on 100 matching coordinates manually annotated in QuPath (Bankhead et al. 2017).

#### 2.3.5.2 Automatic feature detection

To address the limitations of manual annotation, we explored two automatic feature detection algorithms from the OpenCV
library (Bradski n.d.): ORB (Oriented FAST and Rotated BRIEF) and SIFT (Scale-Invariant Feature Transform). For ORB descriptors, we employed a brute-force matcher to identify common features while for SIFT descriptors, we utilized a FLANN-based (Fast Library for Approximate Nearest Neighbors) matcher. These methods automatically identify key point descriptors and match the common ones, allowing to estimate the transformation using the RANSAC (Random Sample Consensus) algorithm.

#### 2.3.5.3 Intensity-based optimization method

Another approach to estimate the transformation without manual feature definition is by using intensity-based optimization. For this comparison, we used only the rigid transformation component of IntegrAlign, implemented with SimpleITK (Yaniv et al. 2018). This method corrects for rotation and translation but does not allow for local image deformation.

By comparing these methods with IntegrAlign, we assessed its performance and efficiency, highlighting the differences and potential advantages of our tool in various alignment scenarios.

### 2.3.6 Mature dendritic cells ground truth coordinates

We established the ground truth coordinates for mature dendritic cells (DC-LAMP+ cells) by manually annotating the common DC-LAMP positive cells across two serial slides. This annotation process involved checking each coordinate across the entire slide using QuPath q (Bankhead et al. 2017) and recording indices for cells that match in both slides. Of the 123 and 182 mature dendritic cells detected on each of the two mIF slides, 28 were manually identified as common between both slides.

## 3 Results

### 3.1 On simulated serial slides, the alignment error is smaller than the diameter of the cell nucleus

A natural approach to quantifying the accuracy of IntegrAlign (the alignment error) is to compare the cell coordinates following IntegrAlign alignment to ground truth coordinates. Because ground truth coordinates are generally unknow, we start with mIF slide and the artificially deformed slide: this allows tracking the coordinates of points in the original and deformed images, so that we can compare slides post-realignment to a ground truth (the applied deformation).

We first evaluate IntegrAlign accuracy using four breast patient slides. For each slide, we simulate serial slides by applying the same composite transformation model used for alignment (Fig. 4A). For each initial breast tumor image, we thus generate three different deformed slides and their corresponding ground truth transformations depending on the maximum displacement of the control points (100, 500, and 1000 µm) in the B-spline transformation.

Deformed slides were then aligned to the original images and the composite transformation estimated using IntegrAlign. To quantify alignment accuracy, alignment error in µm, we manually defined 100 points in the tissue of the original slide and transformed them in the artificial serial slide (Fig. 4B) using the ground truth transformation (artificial) and the transformation from IntegrAlign alignment. From the transformed coordinates, we computed misalignment distances between ground truth points and points post-transformation.

Larger deformations reduced alignment accuracy (Fig. 4C). Yet, even with large deformations (1000 µm displacement),

misalignment distance remained low, smaller than the diameter of cell nuclei (6 µm). This indicates robust performance of the alignment.

### 3.2 Alignment accuracy on two serial slides depends on the resolution of deformation and can be optimized by clipping non-alignable tissue areas

We next tested IntegrAlign on authentic serial slides to evaluate its performance on real-world data.

Five pairs of serial slides were aligned using only rigid transformations (step 1) or combining rigid and B-spline transformation (steps 1 & 2, IntegrAlign pipeline). For the B-spline transformation, we varied the mesh size parameter from 1 to 32, allowing for different levels of deformation complexity (a higher mesh size allows more complex deformations).

Augmenting the mesh size increased execution time exponentially (Fig. 5A—left) while pixel-wise correlation (inverse of the similarity metric at convergence) tended to level-off (Fig. 5A—right). To quantify the alignment accuracy, we manually annotated 20 common features between each pair of serial slides using QuPath (Bankhead et al. 2017), as illustrated in Fig. 5B. These common features provided us with corresponding ground truth coordinates for each slide, allowing the computation of the alignment error (Fig. 5C). Increasing the mesh size from 1 to 8 initially increased accuracy, but with higher mesh sizes deteriorated alignment precision in a slide-specific manner (Fig. 5C). As shown in the deformation grid and by quantifying expansions and compressions using the determinant of the Jacobian (Section 2), a mesh size of 8 induced coherent deformations, while a higher mesh size infers erratic deformations inside the tissue (Fig. 5D, Fig. S1A, available as supplementary data at Bioinformatics online).

Examining alignment at single-cell resolution by displaying serial slides side-by-side (Napari dual viewer, Fig. 2C) and mirroring the cursor of the fixed slide into the moving slide showed alignment was often problematic in regions with folds or tearing present in one slide but not in the other (see examples in Fig. S1, available as supplementary data at Bioinformatics online). Clipping of these zones resulted in a decrease of the overall alignment error (Fig. 5E).

### 3.3 The precision alignment provided by IntegrAlign opens the door to combined spatial analyses, including nearest neighbor analysis

The ultimate goal of integrating serial slides employing multiple mIF panels is to perform combined spatial analysis on extended cell types and phenotypes. To evaluate the feasibility of spatial analyses combining serial slides, we performed nearest neighbor analysis on a pair of serial slides (breast tissue). We computed the distances between CD8⁺ T cells and closest tumor cells (CK positive) from slide 1 (CD8.1/CK.1) and slide 2 (CD8.2/CK.2) within each slide, to serve as ground truth. We then performed the same analysis between CD8⁺ T cells from the slide 1, transformed to the coordinate system of slide 2, and tumor cells of slide 2 (CD8.TR1-2/CK.2), combining them into a single coordinate system (Fig. 6A and B). Nearest neighbor distances distributions were similar across these two scenarios (Fig. 6C). For example, median CD8-tumor cell distances were 23.1 µm (IC: [21.5, 24.4]) and 22.4 µm (IC: [21.4, 23.3]) within slide 1 and slide 2, respectively, compared to 22.5 µm (IC: [21.4, 23.8]) across aligned slides. The median absolute difference
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Alignment of an image with an artificially deformed version. (A) Random computational deformation of an original slide (blue slide) using B-spline (in this example, a maximum displacement of 1000 µm is shown) and rigid transformations, generating an artificial serial slide (red slide). Then, IntegrAlign alignment is processed between the two synthetic serial slides resulting in the corresponding IntegrAlign transformation (B) Evaluation of the IntegrAlign alignment accuracy of the original slide onto the artificial serial slide. A random selection of 100 coordinates in the original slide's tissue are converted by the artificial transformation or the IntegrAlign transformation into the artificial serial slide. Finally, the distances between transformed coordinates are calculated. (C) Box-plots showing the distance between coordinates from the original slide converted with the artificial transformation (from B-spline deformation with 100, 500, or 1000 µm of maximum displacement; mesh size = 3) and the corresponding coordinates converted with IntegrAlign transformation (coming from alignment). Shown are *n* = 4 representative pairs of synthetic serial slides. Red dotted line represents the mean diameter of cell nuclei as a reference.</figcaption></figure>

between distances of CD8.1/CK.1 and CD8.TR1-2/CK.2 was
4.6 µm (Fig. 6D and Fig. S2, available as supplementary data
at Bioinformatics online), an acceptable error relative to the
median CD8-tumor cell distance (23.1 µm).

3.4 Unprecedented alignment accuracy using
IntegrAlign compared to existing
alignment methods

To benchmark IntegrAlign together with other alignment meth-
ods, we used two serial slides from one breast cancer patient
and compared alignment based on (i) manual annotation of
common features, (ii) ORB and SIFT (Bradski n.d.), two auto-
matic features detection algorithms, (iii) rigid transformation
(IntegrAlign alignment—step 1 only), and (iv) the complete
IntegrAlign pipeline (Rigid + B-spline transformations).

Ground truth coordinates were defined by manually anno-
tating the DC-LAMP⁺ cells (corresponding to mature den-
dritic cells) common to both serial slides (Fig. 7A). By
transforming DC-LAMP positive cells coordinates from slide
no. 1 to slide no. 2 using each estimated transformation
(Fig. 7A) and computing every corresponding misalignment
distance with the ground truth cell coordinates in slide no. 2,
we find that IntegrAlign most accurately aligns serial slides:
alignment error was 7 µm with IntegrAlign compared to 38–
200 µm with most other approaches. Much of this gain seems
to be attributable to step 2 of IntegrAlign (local deformation
with B-spline transformation): the entire IntegrAlign pipeline
reduced the misalignment distance by 92% compared to the
Rigid transformation only (step 1 of IntegrAlign; Rigid-only
error: 89 µm, entire IntegrAlign pipeline: 7 µm). While VALIS

(Gatenbee et al. 2023) reached comparable accuracy in this
benchmark, IntegrAlign demonstrated greater robustness in
more challenging scenarios. For instance, IntegrAlign can au-
tomatically process downscaled images with consistent com-
pression, avoiding misalignment caused by resolution
mismatches, an issue that VALIS (Gatenbee et al. 2023) may
face by default (Fig. S3A, available as supplementary data at
Bioinformatics online). Additionally, some images may con-
tain partially mismatched regions, such as unwanted tissue
areas like tonsil controls (used for immune population refer-
ence) that may be randomly included in one slide but not the
other. In such cases, VALIS (Gatenbee et al. 2023) attempts
to align the entire image (including mismatched regions). In
contrast, thanks to its fast and intuitive manual pre-clipping
and alignment steps, IntegrAlign allows users to clip the non-
overlapping areas and focus alignment on the shared tissue
regions (Fig. S3B). Also, IntegrAlign includes a broader range
of quality control steps that provide users with detailed feed-
back on the alignment process, helping to better assess and
understand the accuracy and reliability of the final result.

4 Discussion

Assessing the accuracy of alignment methods is inherently chal-
lenging due to the lack of reliable common descriptors between
serial slides. Indeed, even if they pertain to one tissue biopsy, the
separation between serial slides (4 µm) results in differences at
the cellular level, making it impossible to perform direct com-
parison by retrieving all cells from one slide and contrasting
them to the other slide. To overcome this, one can manually
<figure><img src="image_6.png" /><figcaption>**Figure 5.** Features and accuracy of IntegrAlign alignment on two serial slides. (A) Execution time and pixel-wise correlation of the alignment computed with different mesh sizes on five pairs of serial slides from different tissues. Rigid transformation alone is shown as a reference. (B) Twenty common features are manually marked on slide 1 (left) and slide no. 2 (right) to compute the distances between the coordinates in slide no. 2 and the transformed coordinates of slide no. 1 in slide no. 2. (C) Alignment error represented by the median distance (µm) between the common features for different mesh sizes. (D) Inverse representation of the position, direction and intensity of deformations induced by the alignment on the resampled target image (deformed target slide) with a deformation grid (left panel) and the map of the Jacobian determinant (red = compression, blue = expansion) for a mesh size value of 8 (top panel) or 16 (bottom panel). (E) Box-plots showing the alignment error with (+) or without (–) manual exclusion (clipping) of non-alignable tissue areas for each pair of serial slides. The pair 5 is not shown as no non-alignable tissue areas were present. Red dotted line represents the mean diameter of cell nuclei as a reference.</figcaption></figure>

define common features between slides. In Fig. 7, we achieved this by manually matching authentic cell coordinates, which is arguably the most representative method we used, and IntegrAlign demonstrated promising results using this approach. Indeed, compared to existing methods that use only rotation and translation, IntegrAlign also incorporates local deformation with the sequential application of Rigid and B-spline transformations from the Simple ITK package. By adopting this two-step strategy, we effectively reduced the risk of unwanted random deformations that may arise when directly applying the B-spline transformation to severely misaligned images. This approach ensures a smoother optimization process and enhances the overall alignment outcome, providing a more robust and accurate alignment of the images. Also, regarding the alignment itself, compared to the commonly used methods that need annotation of matching control points to estimate the transformation, which is time-consuming and labor-intensive particularly when high precision is required, IntegrAlign alignment is fully automated thanks to the use of the intensity-based optimization algorithm.

IntegrAlign was designed for processing imaging data from large cohorts. We chose the L-BFGS-B optimizer which efficiently approximates the inverse Hessian matrix, without

explicitly computing or storing it. This is beneficial since we are dealing with large-scale optimization problems where memory usage is a concern. This keeps memory requirements manageable while speeding up convergence compared to first-order methods like gradient descent. Among the various similarity metrics that the SimpleITK (Yaniv et al. 2018) package can use to optimize B-spline deformation, we found that correlation optimized computation time while performing well in terms of accuracy (data not shown).

Computing this similarity metric on only a fraction of random sampled pixels combined with transient downscaling the imaging data (DAPI channel) significantly decreased computation time and memory while preserving the information needed for accurate alignment.

Larger mesh size can lead to an exponential increase of the execution time (Fig. 5A—left), an issue when aligning a large number of samples. Larger mesh sizes also endow slide alignment with too much freedom, resulting in “over-alignment” where tissue stretching occurs to match areas that do not correspond because it increases the similarity metric (Fig. 5D). Such discrepancies can arise due to slight technical differences between scanning of serial slides, random staining variations, or differences in tissue content (damaged tissue) between
<figure><img src="image_7.png" /></figure>

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

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

<figure><img src="image_10.png" /><figcaption>**Figure 6.** Nearest neighbor analysis of CD8+ T cells and tumor cells (common markers) between two serial slides aligned by IntegrAlign. (A) Schematic illustration of the nearest neighbor distances of reference CD8+ cells to neighbor tumor (CK+) cells between two serial slides. The analysis includes computation of the distances between CD8+ and nearest CK+ cells within a single slide (no. 1 or no. 2) and also between CD8+ cells from slide no. 1 transformed in slide no. 2 and nearest CK+ cells of slide no. 2. (B) Visual representation of the spatial distribution of cell coordinates in both slides. CK.1, CD8.1 are cells from slide no. 1. CK.2, CD8.2 are cells from slide no. 2. CD8.TR1-2 are the transformed coordinates of CD8.1 in the coordinate system of slide no. 2. (C) Distribution of the nearest neighbor distances for each CD8-CK pair visualized in (B). (D) Absolute value of the distance differential (absolute error) between CD8 of slide no. 1 to the nearest CK of slide no. 1 and CD8 from slide no. 1 in slide no. 2 to the nearest CK of slide no. 2.</figcaption></figure>

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

<figure><img src="image_12.png" /><figcaption>**Figure 7.** Comparative analysis of alignment methods. Comparison of IntegrAlign with other registration methods performed on one pair of serial slides. (A) Manual identification of shared mature dendritic cells (red circles) between two serial slides. Coordinates of slide no. 1 are transformed into slide no. 2 using five different methods: manual (projective transformation of common features annotated manually), ORB and SIFT (projective transformation of common features annotated automatically), rigid (alignment of images based on their pixel intensity with optimization algorithm using translation and rotation), IntegrAlign (alignment of images based on their pixel intensity with optimization algorithm using translation, rotation, and deformation). (B) Box-plots showing the distances between the real dendritic cell coordinates of slide no. 2 compared to the transformed coordinates from slide no. 1 into slide no. 2's coordinate system using the different methods. Welch's t-test indicates that IntegrAlign significantly reduces misalignment distance compared to the Manual method (p = 8.328e-15), ORB method (p = 1.602e-11), SIFT method (p = 3.779e-03), and Rigid method (p = 1.602e-11). Although the difference between IntegrAlign (6.942 µm) and VALIS (7.036 µm) is not statistically significant for this case (p = 0.371), Fig. S3, available as supplementary data at *Bioinformatics* online, highlights that for some cases, IntegrAlign is more robust.</figcaption></figure>

slides. Therefore, mesh sizes ranging from 1 to 10 can optimize computational efficiency without tradeoff in terms of accuracy.

## 4.1 Key considerations

We show in Fig. 7 that based on cells that have been defined as common between both slides the alignment accuracy is close to the cell nucleus diameter, this confirms the high precision of the alignment process, but it does not guarantee that every transformed cell will correspond to the identical cell in the paired serial section, even if cell segmentation is efficient.

As shown in Fig. S4A, available as supplementary data at *Bioinformatics* online, where the segmentation accurately detected all CD8+ cells (cyan staining), there is a large area, at the top left in Panel1, where CD8 signal is observed without any corresponding signal in Panel2. In practice, only about 20% of CD8+ cells are matched between two serial slides (Fig. S4, available as supplementary data at *Bioinformatics* online). It is important to emphasize that sections are cut at approximately 4 µm thickness and it seems to have a far greater impact than initially assumed. This subtle z-axis displacement significantly reduces the likelihood of
detecting the same cell across adjacent slides, even when alignment is accurate. This is why the purpose of IntegrAlign is not to analyse two stainings from two serial sections on the same cell, but rather to integrate phenotyped cells from two distinct panels, enabling more comprehensive neighborhood analysis by incorporating a broader spectrum of cell populations.

In the context of application to large cohorts of samples, it is important to identify specific cases and slides that cannot be aligned beforehand to ensure proper alignment and avoid wasting time.

Also, when dealing with images containing multiple pieces of tissue, alignments often fail due to varying distances between these pieces. A practical solution would be to crop the image to isolate each tissue piece and align them one by one. This ensures more accurate alignment by addressing each tissue section's specific features independently.

As shown in Fig. 1A, the final step of IntegrAlign involves transforming the cell coordinates from the fixed (reference) image to the moving (target) image. Alternatively, we could specifically transform cells coordinates from the moving to the fixed image, but this requires computing the inverse transformations. Therefore, choosing which slide serves as the fixed and moving image is crucial as it determines the target image where all coordinates will be transformed.

## 4.2 Opportunities for improvement

Slides with irregular tissue tearing across the entire slide may not align correctly and should be removed before proceeding. The decision can be made by visualizing the downscaled DAPI images to assess the degree of variation between serial slides. However, if the tearing is regular compared to the other serial slides and affects only a portion of the slide or if there is identified staining issues that can lead to certain areas not being analysed (manual annotation of artifacts), we can address these issues with the annotations of empty regions and artifacts. By integrating the annotations into the coordinate system of the target image, we can retain only the cells located in the unblemished tissue of both slides. Indeed, the combined annotation map will accurately represent the tissue structure ensuring that cells present in one slide but transformed into empty areas or artifact in another can be properly identified and managed. Enabling the removal of cells that are erroneously placed in regions with artifacts or missing tissue will automate image clipping and enhance the accuracy of subsequent analysis and interpretation.

In order to choose the optimal mesh size, we verify that it does not induce too much deformation with the deformation grid and Jacobian analysis. Additionally, we could examine the correlation factor between overlaying Rasters of cell coordinates (from DAPI) from both slides. While the manual annotation of common features is highly time-consuming and labor-intensive, using the correlation factor would be a promising automatic approach for assessing alignment accuracy across the entire slide.

We could extend the alignment process beyond two slides to include three serial slides and increase even more the potential number of identified cell types and therefore the possibilities in the subsequent spatial analyses. While image registration is performed pairwise between two serial slides, when three slides are available, the two outer slides can be aligned onto the middle slide as the reference (the one closest in section depth to the others). This creates a unified

coordinate system across all three slides, enabling the capture of finer details and variations in tissue structure and cellular composition.

Using this method will extend the number of parameters analysed rendering the technology interesting compared to high-dimensional techniques like CODEX and IMC (Giesen et al. 2014), which focus on specific ROIs due to the long acquisition time and associated costs constraints. However, with augmented multiplex immunofluorescence, we analyse whole slides, providing a more comprehensive representation of the spatial architecture. This approach is particularly beneficial in oncology for example, where large cohorts of patients are studied and direct analysis of the entire slides avoid bias from selecting specific regions. The technique is cost-effective, less time-consuming and can be translated to clinical routine in particular to analyse immune infiltrates and biomarkers in whole slides.

With an augmented number of markers, more complex spatial analyses become possible such as the identification of niches with tools like NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology (El Marrahi et al. 2023).

## Acknowledgements

The authors thank the members of the IMMUcan consortium, Bertrand Dubois (Centre de Recherche en Cancérologie de Lyon), and Julie Caramel (Centre de Recherche en Cancérologie de Lyon) for their valuable feedback.

## Author contributions

Leo Hermet (Data curation [lead], Formal analysis [lead], Investigation [lead], Methodology [lead], Visualization [lead], Writing—original draft [equal], Writing—review & editing [equal]), Léo Laoubi (Project administration [supporting], Software [supporting], Validation [lead], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [equal]), Martial Scavino (Data curation [supporting], Formal analysis [supporting], Methodology [supporting], Software [supporting], Visualization [supporting]), Anne-Claire Doffin (Investigation [supporting]), Alexia Gazeu (Investigation [supporting]), Justine Berthet (Investigation [supporting]), Berenice Pillat (Investigation [supporting]), Stephanie Tissot (Resources [equal]), Sylvie Rusakiewicz (Resources [equal]), Marie-Cécile Michallet (Conceptualization [supporting], Funding acquisition [supporting]), Jenny Valladeau-Guilemond (Conceptualization [supporting], Funding acquisition [supporting]), Jean Hausser (Conceptualization [supporting], Supervision [supporting], Validation [supporting], Writing—original draft [supporting]), Christophe Caux (Conceptualization [supporting], Funding acquisition [lead], Resources [equal], Supervision [supporting], Writing—original draft [supporting], Writing—review & editing [supporting]), and Margaux Hubert (Conceptualization [lead], Funding acquisition [supporting], Project administration [lead], Supervision [lead], Vali-dation [supporting], Writing—original draft [equal], Writing—review & editing [equal])
## Supplementary data

Supplementary data is available at *Bioinformatics* online.

Conflict of interest: None declared.

## Funding

This work was financially supported by grants from the Ligue Nationale contre le cancer 399 (EL2020.LNCC-CHC), the Fondation ARC pour la Recherche sur le Cancer (grant Sign'IT No. ARCSIGNIT2023030006402), the Région Rhône Alpes (IRICE Project: RRA18-010792-01-10365), and the INCa-DGOS (PRTK No. 2022-186). This work was performed within the framework of the SIRIC project (LYriCAN+, INCa-DGOS-INSERM-ITMO cancer_18003), the LABEX DEVweCAN (ANR-10-LABX-0061), and the IMMUcan project. The IMMUcan project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 821558. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA. IMI.europa.eu.

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