Two-photon Excited Autofluorescence Research Articles

Probing cell metabolism using the two-photon excitation autofluorescence lifetime of tryptophan.

Compared to intensity detection, fluorescence lifetime has the advantage of being unaffected by variations in excitation intensity, fluorophore concentration, or attenuation due to biological absorption and scattering. In this Letter, to the best of our knowledge, we present the use of the two-photon excitation autofluorescence lifetime imaging of tryptophan (TRP) to probe cell metabolism for the first time. Tests of pure chemical samples showed that the fluorescence lifetime of TRP was highly sensitive to changes in molecular conformation and the environment. In in vitro cell experiments, we successfully utilized the fluorescence lifetime of TRP to distinguish tumor cells from healthy cells, track the therapeutic effect of the tumor immunotherapy drug 1-MT for HeLa cells, and monitor cells in response to carbonyl cyanide 3-chlorophenylhydrazone (CCCP)-induced cell apoptosis. These results reveal that the two-photon excitation autofluorescence lifetime of TRP could be a sensitive natural probe of cell metabolism in living cells.

Uniaxial mechanical stretch properties correlated with three-dimensional microstructure of human dermal skin.

The intact and healthy skin forms a barrier to the outside world and protects the body from mechanical impact. The skin is a complex structure with unique mechano-elastic properties. To better direct the design of biomimetic materials and induce skin regeneration in wounds with optimal outcome, more insight is required in how the mechano-elastic properties emerge from the skin's main constituents, collagen and elastin fibers. Here, we employed two-photon excited autofluorescence and second harmonic generation microscopy to characterize collagen and elastin fibers in 3D in 24 human dermis skin samples. Through uniaxial stretching experiments, we derive uni-directional mechanical properties from resultant stress-strain curves, including the initial Young's modulus, elastic Young's modulus, maximal stress, and maximal and mid-strain values. The stress-strain curves show a large variation, with an average Young's modules in the toe and linear regions of 0.1 MPa and 21 MPa. We performed a comprehensive analysis of the correlation between the key mechanical properties with age and with microstructural parameters, e.g., fiber density, thickness, and orientation. Age was found to correlate negatively with Young's modulus and collagen density. Moreover, real-time monitoring during uniaxial stretching allowed us to observe changes in collagen and elastin alignment. Elastin fibers aligned significantly in both the heel and linear regions, and the collagen bundles engaged and oriented mainly in the linear region. This research advances our understanding of skin biomechanics and yields input for future first principles full modeling of skin tissue.

Mask R-CNN provides efficient and accurate measurement of chondrocyte viability in the label-free assessment of articular cartilage

ObjectiveChondrocyte viability (CV) can be measured with the label-free method using second harmonic generation (SHG) and two-photon excitation autofluorescence (TPAF) imaging. To automate the image processing for the label-free CV measurement, we previously demonstrated a two-step deep-learning method: Step 1 used a U-Net to segment the lacuna area on SHG images; Step 2 used dual CNN networks to count live cells and the total number of cells in extracted cell clusters from TPAF images. This study aims to develop one-step deep learning methods to improve the efficiency of CV measurement. MethodTPAF/SHG images were acquired simultaneously on cartilage samples from rats and pigs using two-photon microscopes and were merged to form RGB color images with red, green, and blue channels assigned to emission bands of oxidized flavoproteins, reduced forms of nicotinamide adenine dinucleotide, and SHG signals, respectively. Based on the Mask R-CNN, we designed a deep learning network and its denoising version using Wiener deconvolution for CV measurement. ResultsUsing training and test datasets from rat and porcine cartilage, we have demonstrated that Mask R-CNN-based networks can segment and classify individual cells with a single-step processing flow. The absolute error (difference between the measured and the ground-truth CV) of the CV measurement using the Mask R-CNN with or without Wiener deconvolution denoising reaches 0.01 or 0.08, respectively; the error of the previous CV networks is 0.18, significantly larger than that of the Mask R-CNN methods. ConclusionsMask R-CNN-based deep-learning networks improve efficiency and accuracy of the label-free CV measurement.

Short-wavelength excitation two-photon intravital microscopy of endogenous fluorophores.

The noninvasive two-photon excitation autofluorescence imaging of cellular and subcellular structure and dynamics in live tissue could provide critical in vivo information for biomedical studies. However, the two-photon microscopy of short-wavelength endogenous fluorophores, such as tryptophan and hemoglobin, is extremely limited due to the lack of suitable imaging techniques. In this study, we developed a short-wavelength excitation time- and spectrum-resolved two-photon microscopy system. A 520-nm femtosecond fiber laser was used as the excitation source, and a time-correlated single-photon counting module connected with a spectrograph was used to provide time- and spectrum-resolved detection capability. The system was specially designed for measuring ultraviolet and violet-blue fluorescence signals and thus was very suitable for imaging short-wavelength endogenous fluorophores. Using the system, we systematically compared the fluorescence spectra and fluorescence lifetimes of short-wavelength endogenous fluorophores, including the fluorescent molecules tyrosine, tryptophan, serotonin (5-HT), niacin (vitamin B3), pyridoxine (vitamin B6), and NADH and the protein group (keratin, elastin, and hemoglobin). Then, high-resolution three-dimensional (3D) label-free imaging of different biological tissues, including rat esophageal tissue, rat oral cheek tissue, and mouse ear skin, was performed in vivo or ex vivo. Finally, we conducted time-lapse imaging of leukocyte migration in the lipopolysaccharide injection immunization model and a mechanical trauma immunization model. The results indicate that the system can specifically characterize short-wavelength endogenous fluorophores and provide noninvasive label-free 3D visualization of fine structures and dynamics in biological systems. The microscopy system developed here can empower more flexible imaging of endogenous fluorophores and provide a novel method for the 3D monitoring of biological events in their native environment.

Persistent homology analysis distinguishes pathological bone microstructure in non-linear microscopy images

We present a topological method for the detection and quantification of bone microstructure from non-linear microscopy images. Specifically, we analyse second harmonic generation (SHG) and two photon excited autofluorescence (TPaF) images of bone tissue which capture the distribution of matrix (fibrillar collagen) structure and autofluorescent molecules, respectively. Using persistent homology statistics with a signed Euclidean distance transform filtration on binary patches of images, we are able to quantify the number, size, distribution, and crowding of holes within and across samples imaged at the microscale. We apply our methodology to a previously characterized murine model of skeletal pathology whereby vascular endothelial growth factor expression was deleted in osteocalcin-expressing cells (OcnVEGFKO) presenting increased cortical porosity, compared to wild type (WT) littermate controls. We show significant differences in topological statistics between the OcnVEGFKO and WT groups and, when classifying the males, or females respectively, into OcnVEGFKO or WT groups, we obtain high prediction accuracies of 98.7% (74.2%) and 77.8% (65.8%) respectively for SHG (TPaF) images. The persistence statistics that we use are fully interpretable, can highlight regions of abnormality within an image and identify features at different spatial scales.

Numerical investigation of coherent supercontinuum generation via soliton implosion in ZBLAN photonic crystal fiber

We numerically investigate coherent supercontinuum (SC) generation extending from the deep ultraviolet to the mid-infrared spectral region using few-cycle optical pulses, pumped into a ZBLAN photonic crystal fiber. The finite difference method is used to optimize the fiber structure and achieve two zero dispersion wavelengths, with a broad anomalous dispersion region. The propagation of ultrashort pulses and their nonlinear dynamics is accurately modeled by employing the forward unidirectional propagation equation for the analytic signal of the pulse electric field. We show that by pumping in the anomalous regime of dispersion, the high-order solitons undergo an implosion process and spectral broadening is achieved via the contribution of self-phase modulation, self-steepening, and shock formation, along with a strong transfer of energy into a nonsolitonic resonant Cherenkov-like radiation in the normal dispersion region. Furthermore, numerical results indicate that broadband supercontinua spanning the region from 0.22 to 4.6 μm is successfully achieved with a pulse having a soliton order and duration of 12 and 6 fs, respectively. In addition, the deterministic feature of the implosion mechanism exhibits very low sensitivity to the input quantum noise and achieves excellent coherence properties. The proposed multioctave SC source is found promising for various potential applications, such as two-photon excited autofluorescence, bioimaging, spectroscopy, optical signal processing, and medicine.

Label-free metabolic imaging for sensitive and robust monitoring of anti-CD47 immunotherapy response in triple-negative breast cancer

BackgroundImmunotherapy is revolutionizing cancer treatment from conventional radiotherapies and chemotherapies to immune checkpoint inhibitors which use patients’ immune system to recognize and attack cancer cells. Despite the huge clinical success...

Changes in the microstructure of the human aortic medial layer under biaxial loading investigated by multi-photon microscopy

Understanding the correlation between tissue architecture, health status, and mechanical properties is essential for improving material models and developing tissue engineering scaffolds. Since structural-based material models are state of the art, there is an urgent need for experimentally obtained structural parameters. For this purpose, the medial layer of nine human abdominal aortas was simultaneously subjected to equibiaxial loading and multi-photon microscopy. At each loading interval of 0.02, collagen and elastin fibers were imaged based on their second-harmonic generation signal and two-photon excited autofluorescence, respectively. The structural alterations in the fibers were quantified using the parameters of orientation, diameter, and waviness. The results of the mechanical tests divided the sample cohort into the ruptured and non-ruptured, and stiff and non-stiff groups, which were covered by the findings from histological investigations. The alterations in structural parameters provided an explanation for the observed mechanical behavior. In addition, the waviness parameters of both collagen and elastin fibers showed the potential to serve as indicators of tissue strength. The data provided address deficiencies in current material models and bridge multiscale mechanisms in the aortic media. Statement of significanceAvailable material models can reproduce, but cannot predict, the mechanical behavior of human aortas. This deficiency could be overcome with the help of experimentally validated structural parameters as provided in this study. Simultaneous multi-photon microscopy and biaxial extension testing revealed the microstructure of human aortic media at different stretch levels. Changes in the arrangement of collagen and elastin fibers were quantified using structural parameters such as orientation, diameter and waviness. For the first time, structural parameters of human aortic tissue under continuous loading conditions have been obtained. In particular, the waviness parameters at the reference configuration have been associated with tissue stiffness, brittleness, and the onset of atherosclerosis.

Multimodal spectroscopic imaging : A new, powerful tool for intraoperative tumor diagnostics

The increasing number of cancer cases requires new imaging approaches for intraoperative tumor characterization. Utilization of new optical/photonic methods in combination with artificial intelligence (AI) approaches to address urgent challenges in clinical pathology in terms of intraoperative computational spectral histopathology. Multimodal nonlinear imaging by combining the spectroscopic methods coherent anti-Stokes Raman scattering (CARS), two-photon excited autofluorescence (TPEF), fluorescence lifetime imaging microscopy (FLIM), and second harmonic generation (SHG). By using multimodal spectroscopic imaging, tissue morphochemistry, i.e., its morphology and molecular structure can be visualized in alabel-free manner. The multimodal images can be automatically analyzed using AI-based image analysis approaches. For clinical application in terms of frozen section diagnostics or in vivo use, the presented multimodal imaging approach can be translated into acompact microscope or endoscopic probe concepts. The synergistic combination of spectroscopic imaging modalities in combination with automated data analysis has great potential for fast and precise tumor diagnostics e.g., in terms of precise surgical guidance in laser or robotic surgery. Overall, intraoperative multimodal spectroscopic imaging may represent an innovative advancement for tumor diagnostics in the future, directly leading to improved patient care and significant cost savings.

Multi-photon microscopy for the evaluation of interstitial fibrosis in extended criteria donor kidneys: A proof-of-concept study.

To evaluate the initial use of label-free second harmonic generation (SHG) imaging with two-photon excitation (2PE) auto-fluorescence in multiphoton microscopy (MPM) for the quantification of collagen/fibrosis on preimplantation biopsies of extended criteria donors (ECD). Twenty preimplantation core biopsies were extracted from 10 donor kidney samples, of which originated from seven donors. Kidney Donor Profile Index (KDPI) and Remuzzi scores of biopsies were calculated. Collagen parameters measured included quantification by the Collagen Area Ratio in Total Tissue (CART) and qualitative measurements by Collagen Reticulation Index (CRI). Biopsies classified with>85% KDPI scores had significantly higher CART (p=.011) and lower CRI values (p=.025) than biopsies with ≤ 85% KDPI scores. Increase in CRI values correlated significantly with rise in recipient creatinine levels 1-year post-transplant (p=.027; 95% CI: 4.635-66.797). MPM is an evolving technology that enables the quantification of the amount (CART) and quality (CRI) of collagen deposition in unstained preimplantation biopsies of donor kidneys stratified by KDPI scores. This initial evaluation found significant differences in both parameters between donor kidneys with more or less than 85% KDPI.

Amyloid fibrils in superstructures - local ordering revealed by polarization analysis of two-photon excited autofluorescence.

Protein misfolding products - amyloids - tend to form distinct fibrillar structures of the characteristic fold for a given neurodegenerative disease or pathology. Moreover, amyloids (also in the intermediate or distorted state) can act as secondary nuclei for de novo fibrillation. Such secondary nucleation amplifies plaque development correlated with various diseases. Therefore, a versatile and non-destructive method of detection and differentiation between distinct fibrillar structures is of great importance. Amyloids exhibit unique optical properties, i.e. green-blue autofluorescence, which can also be induced by two-photon excitation. Herein, we use this label-free technique to resolve local fibrillar ordering in amyloid superstructures - spherulites. With polarization-dependent two-photon excited amyloid autofluorescence, we resolved fibrillar orientation in the spherulite corona and discussed the presence of amorphous aggregates, distorted fibrils or amyloid intermediate species within the spherulite core. Our polarization sensitive two-photon microscopy investigations are supported by TEM imaging and provide a promising tool for the detection and differentiation between well-developed amyloid fibrils and amorphous/distorted structures present at different stages of the formation of amyloid superstructures and plaques.

Deep learning provides high accuracy in automated chondrocyte viability assessment in articular cartilage using nonlinear optical microscopy.

Chondrocyte viability is a crucial factor in evaluating cartilage health. Most cell viability assays rely on dyes and are not applicable for in vivo or longitudinal studies. We previously demonstrated that two-photon excited autofluorescence and second harmonic generation microscopy provided high-resolution images of cells and collagen structure; those images allowed us to distinguish live from dead chondrocytes by visual assessment or by the normalized autofluorescence ratio. However, both methods require human involvement and have low throughputs. Methods for automated cell-based image processing can improve throughput. Conventional image processing algorithms do not perform well on autofluorescence images acquired by nonlinear microscopes due to low image contrast. In this study, we compared conventional, machine learning, and deep learning methods in chondrocyte segmentation and classification. We demonstrated that deep learning significantly improved the outcome of the chondrocyte segmentation and classification. With appropriate training, the deep learning method can achieve 90% accuracy in chondrocyte viability measurement. The significance of this work is that automated imaging analysis is possible and should not become a major hurdle for the use of nonlinear optical imaging methods in biological or clinical studies.

Selective Imaging of Microplastic and Organic Particles in Flow by Multimodal Coherent Anti-Stokes Raman Scattering and Two-Photon Excited Autofluorescence Analysis

Microplastic pollution is an urgent global issue. While spectroscopic techniques have been widely used for the identification of plastics collected from aquatic environments, these techniques are often labor-intensive and time-consuming due to sample collection, preparation, and long measurement times. In this study, a method for the two-dimensional detection and classification of flowing microplastic and organic biotic particles with high spatial and temporal resolutions has been proposed based on the simultaneous detection of coherent anti-Stokes Raman scattering (CARS) and two-photon excited autofluorescence (TPEAF) signals. Poly(methyl methacrylate) (PMMA), polystyrene (PS), and low-density polyethylene (LDPE) particles with sizes ranging from several tens to hundreds of micrometers were selectively detected in flow with an average velocity of 4.17 mm/s by CARS line scanning. With the same flow velocity, flowing PMMA and alga particles were measured using a multimodal system of CARS and TPEAF signals. The average intensities of both PMMA and alga particles in the CARS signals at a frequency of 2940 cm-1 were higher than the background level, while only algae emitted TPEAF signals. This allowed the classification of PMMA and alga particles to be successfully performed in flow by the simultaneous detection of CARS and TPEAF signals. With the proposed method, the monitoring of microplastics in a continuous water flow without collection or extraction is possible, which is game-changing for the current sampling-based microplastic analysis.

Two-Photon Excited Polarization-Dependent Autofluorescence of Amyloids as a Label-Free Method of Fibril Organization Imaging.

Amyloids are broadly investigated protein misfolding products with characteristic β-sheet assemblies that have an important role in neurodegenerative diseases (e.g., Alzheimer’s disease). While they are usually visualized by staining with Thioflavin-T, Congo Red, or other fluorescent markers, it still arouses a controversy over possible staining molecule influence on the amyloid structure or aggregation process. In this work we present, for the first time, the polarization analysis of two-photon excited autofluorescence of amyloids and confirm that polarization dependence of the observed emission can be correlated with the orientation of fibrils. We show the potential of two-photon excited autofluorescence for resolution of molecular organization of fibrils within amyloid superstructures. This label-free method is compatible with two-photon imaging already applied in investigation of neurodegeneration model in mice.

Diagnosing Hirschsprung disease by detecting intestinal ganglion cells using label-free hyperspectral microscopy

Hirschsprung disease (HD) is a congenital disorder in the distal colon that is characterized by the absence of nerve ganglion cells in the diseased tissue. The primary treatment for HD is surgical intervention with resection of the aganglionic bowel. The accurate identification of the aganglionic segment depends on the histologic evaluation of multiple biopsies to determine the absence of ganglion cells in the tissue, which can be a time-consuming procedure. We investigate the feasibility of using a combination of label-free optical modalities, second harmonic generation (SHG); two-photon excitation autofluorescence (2PAF); and Raman spectroscopy (RS), to accurately locate and identify ganglion cells in murine intestinal tissue without the use of exogenous labels or dyes. We show that the image contrast provided by SHG and 2PAF signals allows for the visualization of the overall tissue morphology and localization of regions that may contain ganglion cells, while RS provides detailed multiplexed molecular information that can be used to accurately identify specific ganglion cells. Support vector machine, principal component analysis and linear discriminant analysis classification models were applied to the hyperspectral Raman data and showed that ganglion cells can be identified with a classification accuracy higher than 95%. Our findings suggest that a near real-time intraoperative histology method can be developed using these three optical modalities together that can aid pathologists and surgeons in rapid, accurate identification of ganglion cells to guide surgical decisions with minimal human intervention.

Two-photon excited fluorescence (TPEF) may be useful to identify macrophage subsets based on their metabolic activity and cellular responses in atherosclerotic plaques

Background and aimsAtherosclerosis is characterized by the formation of lipid plaques within the arterial wall. In such plaques, the massive and continuous recruitment of circulating monocyte-derived macrophages induces inflammation, leading to plaque destabilization and rupture. Plaque vulnerability is linked to the presence of (i) a large lipid core that contains necrotic, “foamy” macrophages (FMs), (ii) a thin fibrous cap that cannot limit the prothrombotic lipid core, and potentially (iii) an imbalance between inflammatory and immunoregulatory macrophages. These opposite macrophage functions rely on the use of different energy pathways (glycolysis and oxidative phosphorylation, respectively) that may lead to different levels of the auto-fluorescent cofactors nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD). We hypothesized that high-resolution two-photon excited autofluorescence (TPEF) imaging of these cofactors may be used to monitor the metabolic activity and cellular responses of macrophages in atherosclerotic plaques. MethodsDifferent models of human FMs were generated by exposure to acetylated or oxidized low-density lipoproteins (LDL), with/without human carotid extract (CE). Their phenotype and optical properties were compared with those of extremely polarized macrophages, inflammatory M1 (MLPS+IFNγ) and immunoregulatory M2 (MIL4+IL13). ResultsThese FM models displayed an intermediate phenotype with low levels of M1 and M2 “specific” markers. Moreover, the NADH and FAD autofluorescence profiles of FMoxLDL ± CE cells were significantly distinct from those of M1 and M2 macrophages. ConclusionsTPEF imaging may be useful to follow the metabolic activity and cellular responses of the different macrophage subtypes present in atherosclerotic plaques in order to detect vulnerable areas.

Nonlabeling and quantitative assessment of chondrocyte viability in articular cartilage with intrinsic nonlinear optical signatures.

Chondrocyte viability is a crucial factor for evaluating cartilage health. Most prevalent cell viability assays rely on dyes and are not applicable for in vivo or longitudinal studies. Here we demonstrated that the two-photon excited autofluorescence and second harmonic generation microscopy provided high-resolution imaging of cartilage tissue and distinguished live/dead chondrocytes by visual assessment. Furthermore, the normalized autofluorescence ratio was proposed as a quantitative indicator to determine chondrocyte viability. Based on the indicator, a curve fitting and simulated receiver operating characteristic method was proposed to identify the live/dead cell populations as well as the indicator threshold without dye labeling. Thus, it established the label-free imaging method for chondrocyte viability assay in cartilage tissue. Impact statement Chondrocytes are the only cellular component found in the cartilage, playing a critical role in maintaining the homeostasis of articular cartilage. The viability of chondrocytes is a crucial factor for evaluating cartilage health. However, the current prevalent cell viability assays rely on dye staining and thereby are not applicable in vivo or in longitudinal assessments. In this study, we demonstrate that the intrinsic signals such as two-photon excited autofluorescence and second harmonic generation can be used to classify live and dead chondrocytes in cartilage tissue. A quantitative measure is also proposed allowing development of automated assessment algorithms. The nonlabeling nature of this method suggests the potential applicability to nondestructive and in vivo assessment of cartilage health.

Label-free whole-colony imaging and metabolic analysis of metastatic pancreatic cancer by an autoregulating flexible optical system

Cancer metastasis is a Gordian knot for tumor diagnosis and therapy. Many studies have demonstrated that metastatic processes are inevitably affected by the tumor microenvironment. Histopathology is used universally as the gold standard for cancer diagnosis despite the lengthy preparation process and invasiveness.Methods: Here, we introduced a supercontinuum and super-wide-tuning integrated multimodal platform, which combines the confocal, nonlinear and fluorescence lifetime microscopy with autoregulations, for label-free evaluation of fresh tissue and pathological sections. Based on various automated tunable lasers, synchronized and self-adjusting components and eight fast switching detection channels, the system features fast, large-field and subcellular-scale imaging of exogenous and endogenous fluorophores, nonlinear coherent scattering and lifetime contrast.Results: With such an integrated multi-dimensional system, we searched the metastatic region by two-photon and three-photon excited autofluorescence, analyzed the cancer invasion by second harmonic generation and revealed the affected cellular metabolism by phasor-lifetime. We demonstrated the flexible measurement of multiple nonlinear modalities at NIR I and II excitation with a pre-compensation for group delay dispersion of ~7,000 fs2 and low power of <40 mW, and of dual autofluorescence lifetime decays for phasor approach to decompose cancer-associated and disassociated components. This significantly revealed the metastatic and metabolic optical signatures of the whole colony of pancreatic cancers.Conclusion: The synergistic effect of the system demonstrates the great potential to translate this technique into routine clinical applications, particularly for large-scale and quantitative studies of metastatic colonization.

In vivo identification of arteries and veins using two-photon excitation elastin autofluorescence.

Distinguishing arteries from veins in vivo has a great significance in clinical practices and preclinical studies. Optical imaging methods such as two-photon microscopy can provide high-resolution morphological information of tissue and are therefore extremely suitable for imaging small blood vessels. However, few optical imaging methods allow in vivo identification of arteries and veins merely utilizing the autofluorescence signal of blood vessels. In this report, we found the arterial wall generates a remarkably stronger two-photon excitation autofluorescence (TPEA) signal compared with the venous wall based on BALB/c mice. According to histological analysis and fluorescence characteristic measurement, the contrast signal is confirmed to be from elastin fibers. Employing this unique feature, we propose an objective and effective artery-vein separation strategy that considers the presence of the elastin-TPEA border as the indicator of arteries. Using this strategy, the arterial and venous networks of the dorsal skin and cerebral cortex of BALB/c mice are demonstrated to be excellently mapped and accurately separated in vivo without depending on any exogenous contrast agent, empirical knowledge, and algorithm. This study may provide a novel technique for mapping arterial and venous networks for anatomic research as well as an extra aid to basic researches on the mechanism, diagnosis, and treatment of blood vessel-related diseases.

Nonlinear Multimodal Imaging Characteristics of Early Septic Liver Injury in a Mouse Model of Peritonitis.

Sepsis constitutes a life-threatening organ failure caused by a deregulated host response to infection. Identifying early biomolecular indicators of organ dysfunction may improve clinical decision-making and outcome of patients. Herein we utilized label-free nonlinear multimodal imaging, combining coherent anti-Stokes Raman scattering (CARS), two-photon excited autofluorescence (TPEF), and second-harmonic generation (SHG) to investigate the consequences of early septic liver injury in a murine model of polymicrobial abdominal infection. Liver tissue sections from mice with and without abdominal sepsis were analyzed using multimodal nonlinear microscopy, immunofluorescence, immunohistochemistry, and quantitative reverse transcription polymerase chain reaction (qRT-PCR). Twenty-four hours after the induction of sepsis, hepatic mRNA of inflammatory cytokines and acute phase proteins was upregulated, and liver-infiltrating myeloid cells could be visualized alongside hepatocellular cytoplasmic translocation of high mobility group box 1. According to the statistical analysis based on texture feature extraction followed by the combination of dimension reduction and linear discriminant analysis, CARS (AUC = 0.93) and TPEF (AUC = 0.83) showed an excellent discrimination between liver sections from septic mice and sham-treated mice in contrast to SHG (AUC = 0.49). Spatial analysis revealed no major differences in the distribution of sepsis-associated changes between periportal and pericentral zones. These data suggest early alterations in hepatic lipid distribution and metabolism during liver injury and confirm nonlinear multimodal imaging as a promising complementary method for the real-time, label-free study of septic liver damage.