Seeing Beyond the Surface: Holotomography’s Rise in Biological Imaging

Author: Denis Avetisyan

Holotomography is rapidly evolving from a niche morphometric technique into a powerful, AI-driven platform for label-free, 3D multimodal phenotyping across diverse biological applications.

This review explores the advancements of holotomography, detailing its potential in areas ranging from organoid analysis to clinical diagnostics through quantitative phase imaging and artificial intelligence.

While conventional biomedical imaging often relies on labels or perturbative techniques, holotomography (HT) is emerging as a powerful, label-free alternative. This Holotomography in 2025: From Morphometric Imaging to AI-Driven Multimodal Phenotyping review details HT’s maturation into a versatile platform for quantitative 3D imaging, now significantly enhanced by artificial intelligence and expanding beyond simple morphometry. Recent advances demonstrate HT’s potential in diverse applications, from subcellular phenotyping to early clinical diagnostics, and increasingly, its integration with complementary modalities. Will these developments pave the way for HT to become a standard tool in digital pathology and high-throughput screening workflows?

Beyond Observation: The Pursuit of Quantitative Certainty

Historically, the diagnosis of disease has heavily depended on visual inspection of tissue samples following staining with dyes to highlight specific features. While valuable, this approach introduces inherent subjectivity, as interpretations can vary between pathologists, leading to potential inconsistencies in diagnosis and treatment plans. Furthermore, the process is often time-consuming and limits the ability to analyze large volumes of tissue or conduct high-throughput studies. This reliance on qualitative assessment restricts the extraction of precise, quantifiable data regarding cellular morphology, organization, and molecular expression – information crucial for a deeper understanding of disease mechanisms and the development of more targeted therapies. The limitations of traditional pathology underscore the need for more objective and efficient methods of tissue analysis.

The pursuit of truly quantitative biological understanding increasingly relies on label-free imaging techniques, which offer a powerful alternative to traditional methods dependent on staining and fluorescent markers. These modalities, including techniques like quantitative phase microscopy and digital holographic microscopy, allow researchers to observe cellular phenotypes and dynamic processes without perturbing the living system. By measuring inherent optical properties – such as refractive index and birefringence – label-free imaging reveals subtle morphological changes and intracellular dynamics that might be obscured by conventional labeling. This capability is particularly valuable for longitudinal studies tracking cell behavior over time, assessing drug responses, and unraveling the complexities of cellular signaling pathways with high resolution and minimal invasiveness. The ability to quantify cellular characteristics objectively, rather than relying on subjective interpretations, is driving advancements in diverse fields, from cancer research to regenerative medicine.

Current bioimaging techniques often face limitations when applied to the complex, three-dimensional architecture of living tissues. Many established methods struggle to penetrate beyond the surface of thick samples, scattering light or losing resolution with depth-a significant hurdle for studying whole organs or developmental processes. Furthermore, while images can visually depict cellular structures, extracting quantitative biophysical information – such as cell stiffness, dry mass, or protein concentration – remains a considerable challenge. Simply observing morphological changes isn’t enough; researchers increasingly require precise, numerical data to understand the underlying biological mechanisms driving these changes. Overcoming these limitations demands innovative approaches that can simultaneously achieve deep tissue penetration and detailed biophysical characterization, allowing for a more complete and objective understanding of cellular behavior in situ.

Holotomography: Mapping the Cellular Landscape

Holotomography (HT) generates a three-dimensional map of a sample’s refractive index distribution by analyzing the phase shifts of light as it passes through the specimen. This technique is label-free, meaning no exogenous dyes or markers are required, as variations in refractive index directly correlate with differences in density and composition within the sample. The resulting volumetric data reveals intrinsic biophysical properties such as dry mass, protein concentration, and lipid content without the need for staining or genetic modification, providing a non-destructive method for quantitative analysis of cellular and tissue structures. The reconstructed refractive index map provides detailed information on internal structures and allows for the quantification of these biophysical properties throughout the sample volume.

Holotomography utilizes quantitative phase imaging (QPI) to generate high-resolution, three-dimensional images of cells and tissues. QPI techniques measure variations in phase shifts of light as it passes through a transparent sample, directly correlating these shifts with changes in refractive index. This allows for label-free visualization of internal cellular structures and provides volumetric data sets with resolutions typically ranging from 0.5 to 2 micrometers. Unlike traditional microscopy methods reliant on light absorption or scattering, QPI is sensitive to slight density variations, enabling detailed analysis of dry mass, cell volume, and sub-cellular components without the need for staining or other potentially disruptive sample preparation techniques.

Refractive index tomography forms the core of holotomographic reconstruction by measuring the phase shift of light as it passes through a sample. This phase information is directly related to the sample’s three-dimensional refractive index distribution; areas of differing density and composition exhibit variations in refractive index. Through tomographic acquisition – multiple projections from different angles – an iterative reconstruction algorithm calculates the 3D refractive index map. This map then allows for the visualization of internal cellular structures and the quantitative analysis of morphological features, such as cell volume, shape, and the distribution of organelles, without the need for exogenous labeling or contrast agents. The resulting volumetric data provides detailed insights into cellular composition and organization.

Refining the Signal: Advances in Holotomographic Technique

Modern holotomography techniques, including MuPaSA and Low-Coherence Holotomography (LC-HT), address limitations of conventional HT by improving imaging speed and reducing scattering artifacts. MuPaSA, specifically, achieves a fourfold increase in volumetric acquisition rate, enabling more rapid data collection. This enhanced speed, combined with artifact reduction, expands the applicability of HT to thicker biological samples and allows for the observation of dynamic processes in live cells that were previously inaccessible due to long acquisition times or image degradation from scattering.

Polarization-Sensitive Holotomography (PS-HT) leverages the principle of birefringence – the differing refractive indices of light traveling in different directions within a sample – to enhance visualization of specific intracellular structures. Structures exhibiting birefringence, such as lipid droplets, collagen, and microtubules, alter the polarization state of light passing through them. PS-HT detects these alterations, providing increased contrast and sensitivity compared to standard holotomography. This allows for label-free identification and quantification of birefringent components within cells and tissues, facilitating studies of lipid metabolism, extracellular matrix organization, and cytoskeletal dynamics without the need for exogenous staining or labeling.

Holotomography enables quantitative phase imaging which, when combined with established computational methods, facilitates the precise measurement of mass density within biological samples. This parameter is directly related to refractive index and thickness, allowing for label-free assessment of cellular composition and organization. Variations in mass density can indicate changes in cellular states, such as proliferation, differentiation, or responses to external stimuli. The accuracy of these measurements is influenced by the precision of phase retrieval algorithms and the optical properties of the imaging system, but provides a valuable biophysical contrast mechanism complementary to traditional microscopy techniques.

From Images to Insight: High-Throughput Phenotyping

Automated image analysis, powered by deep learning, is revolutionizing high-throughput (HT) data interpretation by enabling precise phenotypic classification. Recent advances incorporate organelle-aware representation learning, allowing algorithms to focus on key cellular structures, and virtual staining techniques that computationally replicate traditional histological methods. Notably, these virtual staining approaches have demonstrated a high degree of accuracy, achieving a Structural Similarity Index Measure (SSIM) score exceeding 0.75 when compared to data from conventional chemical staining. This level of fidelity allows researchers to bypass time-consuming and resource-intensive manual analysis, accelerating discoveries in areas like drug screening and disease modeling by providing quantitative and reproducible insights from complex biological images.

High-throughput technologies are fundamentally reshaping the pace of biological research by enabling massively parallel experimentation and observation. This capability dramatically accelerates drug discovery processes; instead of evaluating potential therapeutic compounds one at a time, researchers can now screen thousands or even millions of candidates simultaneously. Similarly, disease modeling benefits from the ability to create and analyze complex biological systems in vivo at an unprecedented scale, allowing for more accurate representations of disease progression and response to treatment. The resultant data, generated at speeds previously unattainable, fosters a deeper understanding of biological mechanisms and ultimately expedites the translation of research findings into clinical applications, offering the potential for faster development of effective therapies and diagnostic tools.

High-throughput phenotyping is revolutionizing the study of complex biological systems, notably through its application to organoid models and diagnostic pathology. Researchers are now able to quantitatively assess three-dimensional tissue architecture and function with unprecedented detail, moving beyond traditional qualitative analysis. This approach has yielded significant progress in disease modeling and diagnosis; for example, a DenseNet model achieved an Area Under the Receiver Operating Characteristic curve (AUROC) of greater than 0.89 in distinguishing central nervous system infections. Simultaneously, EfficientNet-b3 models demonstrate a remarkable capacity for cancer cell classification, identifying lipid metabolic signatures with over 98% accuracy – suggesting a powerful new avenue for personalized medicine and targeted therapies based on cellular phenotype.

The Future is Integrative: Beyond Single Measurements

High-throughput technologies, when coupled with multi-omics approaches like spatial transcriptomics and proteomics, are revolutionizing the study of cellular behavior. This integration transcends traditional reductionist biology by allowing researchers to move beyond simply identifying what genes are expressed, to understanding where and how those genes contribute to a cell’s characteristics – its phenotype. Spatial transcriptomics, for example, maps gene expression patterns within the physical context of a tissue, revealing crucial cell-cell interactions and microenvironmental influences. Simultaneously incorporating proteomic data – the complete set of proteins present – provides a functional layer, clarifying which genes are actively translated and contributing to the observed phenotype. This holistic view unveils the complex interplay between genotype, location, and function, offering a far more complete and nuanced understanding of biological systems and paving the way for precision medicine.

High-throughput technologies generate data that is fundamentally quantitative, enabling researchers to move beyond simple observation and into rigorous statistical analysis. This precision is crucial for discerning not just dramatic shifts in biological states, but also the subtle phenotypic changes that often precede or accompany disease development. Sophisticated statistical methods can now tease out meaningful signals from the inherent noise in complex biological systems, revealing previously hidden correlations and patterns. Consequently, researchers are increasingly able to identify biomarkers indicative of early-stage disease, predict treatment responses with greater accuracy, and develop personalized therapies tailored to an individual’s unique molecular profile. The ability to quantify phenotypes at scale represents a paradigm shift, transforming biological research from largely descriptive to powerfully predictive and enabling a deeper understanding of complex biological processes.

The trajectory of high-throughput technologies points toward an era of unprecedented biological insight, driven by continuous refinement of both hardware and analytical approaches. Innovations in microfluidics, sequencing speeds, and detector sensitivity are expanding the scale and resolution of data acquisition, while parallel advancements in machine learning and artificial intelligence are enabling researchers to extract meaningful patterns from increasingly complex datasets. This synergistic progress isn’t merely incremental; it promises to revolutionize the understanding of disease mechanisms, accelerate drug discovery, and facilitate the development of personalized therapies tailored to an individual’s unique molecular profile. Ultimately, these combined capabilities are poised to bridge the gap between fundamental biological research and tangible clinical applications, offering the potential for earlier diagnoses, more effective treatments, and improved patient outcomes.

The pursuit of increasingly detailed biological understanding, as demonstrated by holotomography’s evolution, necessitates acknowledging the inherent limitations of any measurement. This technology, moving beyond simple morphometrics toward AI-driven multimodal phenotyping, doesn’t eliminate uncertainty – it refines its boundaries. Niels Bohr observed, “The opposite of a trivial truth is another trivial truth.” Holotomography’s progression exemplifies this; each improvement in resolution or analytical capability reveals further layers of complexity, demanding continuous recalibration and acceptance of inherent error. The system doesn’t deliver absolute truth, but increasingly precise approximations, acknowledging that even the most advanced models are, at their core, representations – not reality itself.

What Lies Ahead?

The convergence of holotomography and artificial intelligence, as presented, offers intriguing possibilities. However, the field must now confront a critical question: how much of what is being ‘discovered’ is genuine biological signal, and how much is artifact of the reconstruction algorithms themselves? A proliferation of elegant models predicting cellular behavior from refractive index changes will be meaningless without rigorous validation – ideally, predictions that actively fail when applied to sufficiently diverse datasets. If every result confirms the hypothesis, one should suspect the hypothesis – or, more likely, the experimental design.

Expansion into clinical diagnostics, while potentially impactful, demands a level of standardization currently absent. Variations in instrument calibration, sample preparation, and data processing will inevitably introduce biases. The promise of virtual staining is particularly seductive, but the correlation between refractive index and specific biomolecular markers requires far more exhaustive investigation than has been demonstrated. Simply put, demonstrating correlation is not the same as establishing causation.

Ultimately, the true test of holotomography’s maturation will not be the sophistication of its image reconstruction or the cleverness of its AI algorithms, but the ability to generate predictions that can be demonstrably falsified. The field should embrace failure – not as a setback, but as the most reliable path towards understanding. A healthy dose of skepticism, applied both to the data and to the interpretations thereof, will be essential.

Original article: https://arxiv.org/pdf/2601.02611.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/