Mihaela Žigman

Standardized preanalytical sample handling is a cornerstone of reliable molecular analytics, particularly where subtle multimolecular differences can drive biological interpretation. In different clinical studies, blood-based samples are often processed using varying workflows, and the influence of individual technical parameters remains poorly quantified. In particular, preanalytical differences can limit study comparability and complicate the robust evaluation of the approach for different applications and specific medical questions. Here, we systematically investigate the effects of major preanalytical variables on infrared molecular fingerprinting (IMF) of blood serum and plasma, performed via Fourier transform infrared (FTIR) spectroscopy. We examine the impacts of venous blood draw tube types, tube fill volumes, delay times prior to centrifugation, centrifugation conditions, freezing and thawing procedures, and delay times between final sample preparation and infrared spectroscopy. We found that the type of blood collection tube, the fill volume of the tube, a delay of more than 4 h before centrifugation, more than two freeze-thaw cycles, and delays prior to measurement can result in systematic changes in infrared fingerprints that are detectable through multivariate analysis and machine learning. Importantly, for most conditions, the magnitude of parameter-related spectral variation remains smaller than the intrinsic biological variability across individuals, supporting the feasibility of IMF for realistic clinical settings. The presented results provide actionable guidance and a basis for harmonizing preanalytical workflows, reducing preanalytical confounding in IMF-based biofluid analysis, and improving cross-study comparability, with relevance that may generalize to other molecular profiling technologies, clinical studies, and biobanking.

Background: Prostate-specific antigen (PSA) testing has limited specificity, particularly at intermediate PSA concentrations, contributing to diagnostic uncertainty in prostate cancer (PCa). Infrared molecular fingerprinting (IMF) provides rapid plasma-based readout of disease-associated biochemical alterations, and may capture molecular information that is not fully reflected by PSA concentration.Methods: In the prospective Lasers4Life study, plasma from 2,024 men was analysed, including 1,012 treatment-naïve PCa patients, 752 men with benign prostatic hyperplasia (BPH), and 260 asymptomatic controls. Classification models were evaluated using cross-validation and, for PCa-vs-BPH discrimination, temporally independent held-out cohort testing. Diagnostic performance was quantified by the area under the receiver operating characteristic curve (ROC AUC) and examined across PSA strata, comorbidities and medications.Results: IMF discriminated PCa from asymptomatic controls (AUC 0.79 ± 0.08) and from BPH (AUC 0.67 ± 0.04) in cross-validation. In an exploratory age- and PSA-matched subset of individuals with PSA below 12 ng/mL (comprising 200 PCa and 200 BPH cases), IMF showed higher discrimination than PSA alone (AUC 0.63 vs 0.56; p < 1×10⁻⁴). Most examined comorbidities and medications did not strongly alter IMF performance, except prostatitis modestly reduced discrimination.Conclusions: IMF detected PCa-associated molecular signatures in plasma, including signals partly independent of PSA. However, diagnostic discrimination was modest, and clinical utility remains to be proven. Independent validation, clinically significant PCa endpoints, and demonstration of additive value within established diagnostic pathways are required.

In this study, we investigate the individuality and information content of infrared molecular profiles derived from blood samples in a large, longitudinal health-profiling cohort and compare them to a standard clinical laboratory panel. Using Fourier-transform infrared spectroscopy, we obtained comprehensive molecular fingerprints from 4,704 self-reported healthy individuals over five visits spanning 1.5 years, alongside routine clinical laboratory measurements. We show that infrared profiles are highly individual-specific and remarkably stable over time, with intra-individual variability significantly lower than inter-individual differences—paralleling the characteristics observed in clinical laboratory data. To quantify and compare the information content of these molecular datasets, we employ individual identification as a proxy for Shannon entropy. In this framework, higher identification accuracy reflects a higher amount of information. Infrared profiles outperform the clinical laboratory panel in identifying individuals at scale, suggesting higher intrinsic information content. Furthermore, combining infrared and clinical laboratory data substantially improves identification performance (the identification of less than 3000 individuals by the clinical laboratory panel is boosted to more than 4000 by incorporating the infrared spectroscopic markers), highlighting the value of integrating complementary data modalities. These findings suggest a practical framework, rooted in information theory, for comparing molecular profiling approaches and emphasize the potential of infrared spectroscopy as a complementary tool in personalized medicine.

The field of infrared (IR) photonics is currently undergoing remarkable progress, moving rapidly towards practical sensing applications demanded by medical therapy and diagnostics (theranostics). The Developments can be divided into three main categories: (i) novel devices and measurement concepts including advanced updates of classical approaches that push medical sensing into the spotlight; (ii) new demonstrations of photonic integrated circuit (PIC-)based IR devices enabling highly miniaturized sensors for point-of-care application as well as medical and wellness wearables; and (iii) technologically-mature IR demonstrators that enable first medical sensing and treatment applications. This roadmap paper provides a consolidated overview of this highly dynamic and interdisciplinary research field with a focus on the major roadblocks that limit the widespread adoption of IR photonics in large-scale medical diagnostics. Special attention is given to the ambivalence between the molecular-level spectroscopic interpretation and a broader health-state assessment, highlighting the need for a common framework. Additionally, the paper discusses the critical importance of unified measurement standards, calibration protocols, and medical certification processes to ensure the validity of experimental results, reproducibility, and clinical trust, particularly when novel experimental techniques and AI algorithms are involved. Perspectives from major past and current contributors to application-oriented IR photonics will be provided.

This paper presents a solution to the challenge of cross-device model generalization in blood-based infrared spectroscopy. As infrared spectroscopy becomes increasingly popular for analyzing human blood, ensuring that machine learning models trained on one device can be effectively transferred to others is essential. However, variations in device characteristics often reduce model performance when applied across different devices. To address this issue, we propose a straightforward domain adaptation method based on data augmentation incorporating device-specific differences. By expanding the training data to include a broader range of nuances, our approach enhances the model's ability to adapt to the unique characteristics of various devices. We validate the effectiveness of our method through experimental testing on two Fourier-Transform Infrared (FTIR) spectroscopy devices from different research laboratories, demonstrating improved prediction accuracy and reliability.

Human biofluids serve as indicators of various physiological states, and recent advances in molecular profiling technologies hold great potential for enhancing clinical diagnostics. Leveraging recent developments in laser-based electric-field molecular fingerprinting, we assess its potential for in vitro diagnostics. In a proof-of-concept clinical study involving 2533 participants, we conducted randomized measurement campaigns to spectroscopically profile bulk venous blood plasma across lung, prostate, breast, and bladder cancer. Employing machine learning, we detected infrared signatures specific to therapy-naïve cancer states, distinguishing them from matched control individuals with a cross-validation ROC AUC of 0.88 for lung cancer and values ranging from 0.68 to 0.69 for the other three cancer entities. In an independent held-out test data set, designed to reflect different experimental conditions from those used during model training, we achieved a lung cancer detection ROC AUC of 0.81. Our study demonstrates that electric-field molecular fingerprinting is a robust technological framework broadly applicable to disease phenotyping under real-world conditions.

Die Schwingungsspektroskopie ist eine weit verbreitete Technik zur chemischen Charakterisierung in verschiedenen analytischen Disziplinen. Ihre Anwendungen erstrecken sich zunehmend auf die Analyse komplexer Proben wie Bioflüssigkeiten und ermöglichen molekulares Profiling mit hohem Durchsatz. Trotz ihrer Leistungsfähigkeit leidet diese Technologie unter einer inhärenten Einschränkung: Der Überlapp von Absorptionsinformationen über verschiedene Spektralbereiche hinweg behindert die Fähigkeit, einzelne Substanzen zu identifizieren, welche zu den gemessenen Gesamtsignal beitragen. Obwohl man sich dieser Herausforderung bewusst ist, wird die Schwierigkeit der Analyse von Multimolekülspektren oft unterschätzt, was zu Fehlinterpretation führen kann. In dieser Arbeit diskutieren wir kritisch den weit verbreiteten übermäßigen Verlass auf einzelne Absorptionsbanden bei der Dateninterpretation und beleuchten die Fallstricke bei der Korrelation von spektral Signalen mit diskreten Substanzen oder physiologischen Zuständen ohne rigorose Validierung. Mit Fokus auf blutbasierte Infrarotspektroskopie liefern wir Beispiele, die zeigen, wie Überlappungen lokaler Absorptionsmaxima zwischen verschiedenen Substanzen, relative Konzentrationen von Substanzen und Datenvorverarbeitungsschritte zu fehlerhaften Interpretationen führen können. Wir plädieren für einen Paradigmenwechsel hin zu einem vorsichtigeren Verständnis komplexer Spektren, was dazu führen sollte, entweder deren Charakter als molekularen Fingerabdruck zu akzeptieren und maschinelles Lernen zur Analyse zu nutzen – oder zusätzliche Messmodalitäten für eine robuste molekulare Interpretationen einzubeziehen. Mit dem Ziel, analytische Praktiken in diesem Bereich zu verbessern und weiterzuentwickeln, heben wir die Grenzen molekularer Interpretationen hervor und stellen mögliche Anwendungen vor.