空间分辨代谢组学进展和挑战

空间分辨代谢组学即整合质谱成像和代谢组学技术,对动/植物组织和细胞中内/外源性代谢物的种类、含量和差异性空间分布进行精准测定。质谱成像技术因其具有无标记、非特异、高灵敏度、高化学覆盖、元素/分子同时检测等优势,被广泛应用于动/植物组织中各类代谢物、多肽和蛋白的时空分布研究。首先介绍了代谢组学和质谱成像技术的研究现状,然后重点综述了空间分辨代谢组学在动物组织、植物组织和单细胞水平上的前沿应用。最后展望了空间分辨代谢组学技术的现有瓶颈和未来发展方向。空间分辨代谢组学是继代谢组学之后又一门新兴的分子成像组学技术,能够无标记、可视化检测动物组织中外源性药物的吸收、分布、代谢和排泄,以及植物组织中多种代谢产物的生物合成、转运途径和积累规律。该技术将推动靶向药物发现、病理机制解析和动植物生长发育密切关联的空间代谢网络调控等前沿应用研究。     

Cancer stem cell model in oral squamous cell carcinoma

The concept of "field cancerization" describes the presence of histological abnormal tissue surrounding oral squamous cell carcinoma (OSCC). Molecular model of multistep carcinogenesis indicates that an accumulation of genetic alterations forms the basis for the OSCC progression with genetic heterogeneity. Furthermore, we reviewed cancer stem cell (CSC) model, which suggests functional heterogeneity in the tumor mass and current supporting evidence in OSCC. According to CSC model, prevention from carcinogen exposure and eliminating the particular CSCs instead of targeting tumor mass could help obtain a more long-lasting therapeutic effect.     

Multiple statistical analysis techniques corroborate intratumor heterogeneity in imaging mass spectrometry datasets of myxofibrosarcoma

MALDI mass spectrometry can generate profiles that contain hundreds of biomolecular ions directly from tissue. Spatially-correlated analysis, MALDI imaging MS, can simultaneously reveal how each of these biomolecular ions varies in clinical tissue samples. The use of statistical data analysis tools to identify regions containing correlated mass spectrometry profiles is referred to as imaging MS-based molecular histology because of its ability to annotate tissues solely on the basis of the imaging MS data. Several reports have indicated that imaging MS-based molecular histology may be able to complement established histological and histochemical techniques by distinguishing between pathologies with overlapping/identical morphologies and revealing biomolecular intratumor heterogeneity. A data analysis pipeline that identifies regions of imaging MS datasets with correlated mass spectrometry profiles could lead to the development of novel methods for improved diagnosis (differentiating subgroups within distinct histological groups) and annotating the spatio-chemical makeup of tumors. Here it is demonstrated that highlighting the regions within imaging MS datasets whose mass spectrometry profiles were found to be correlated by five independent multivariate methods provides a consistently accurate summary of the spatio-chemical heterogeneity. The corroboration provided by using multiple multivariate methods, efficiently applied in an automated routine, provides assurance that the identified regions are indeed characterized by distinct mass spectrometry profiles, a crucial requirement for its development as a complementary histological tool. When simultaneously applied to imaging MS datasets from multiple patient samples of intermediate-grade myxofibrosarcoma, a heterogeneous soft tissue sarcoma, nodules with mass spectrometry profiles found to be distinct by five different multivariate methods were detected within morphologically identical regions of all patient tissue samples. To aid the further development of imaging MS based molecular histology as a complementary histological tool the Matlab code of the agreement analysis, instructions and a reduced dataset are included as supporting information.     

Changes in the kinetics of dopamine release and uptake have differential effects on the spatial distribution of extracellular dopamine concentration in rat striatum

The objective of this study was to examine whether the limited diffusion distance of dopamine in rat striatum produces spatial heterogeneity in the extracellular dopamine concentration on a dimensional scale of a few micrometers. Such heterogeneity would be significant because it would imply that the concentration of dopamine at a given receptor depends on the receptor's ultrastructural location. Spatially resolved measurements of extracellular dopamine were performed in the striatum of chloral hydrate-anesthetized rats with carbon fiber microdisk electrodes. Dopamine was monitored during electrical stimulation of the nigrostriatal pathway before and after administration of drugs that selectively affect the kinetics of evoked dopamine release and dopamine uptake. The effects of nomifensine (20 mg/kg), L-DOPA (250 mg/kg), and alpha-methyl-p-tyrosine (250 mg/kg) on the amplitude of the stimulation responses were examined. The outcome of these experiments was compared with predictions derived from a mathematical model that combines diffusion with the kinetics of release and uptake. The results demonstrate that the extracellular dopamine concentration is spatially heterogeneous on a micrometer scale and that changing the kinetics of dopamine release and uptake has different effects on this spatial distribution. The impact of these results on brain neurochemistry is considered.     

Histological classification of atherosclerotic arteries using high-speed confocal Raman microscopy with machine learning

Confocal Raman microscopy is a useful tool to observe composition and constitution of label-free samples at high spatial resolution. However, accurate characterization of microstructure of tissue and its application in diagnostic imaging are challenging due to weak Raman scattering signal and complex chemical composition of tissue. We have developed a method to improve imaging speed, diffraction efficiency, and spectral resolution of confocal Raman microscopy. In addition to the novel imaging technique, the machine learning method enables confocal Raman microscopy to visualize accurate histology of tissue sections. Here, we have demonstrated the performance of the proposed method by measuring histological classification of atherosclerotic arteries and compared the histological confocal Raman images with the conventional staining method. Our new confocal Raman microscopy enables us to comprehend the structure and biochemical composition of tissue and diagnose the buildup of atherosclerotic plaques in the arterial wall without labeling.     

High order chromatin architecture shapes the landscape of chromosomal alterations in cancer

The accumulation of data on structural variation in cancer genomes provides an opportunity to better understand the mechanisms of genomic alterations and the forces of selection that act upon these alterations in cancer. Here we test evidence supporting the influence of two major forces, spatial chromosome structure and purifying (or negative) selection, on the landscape of somatic copy-number alterations (SCNAs) in cancer. Using a maximum likelihood approach, we compare SCNA maps and three-dimensional genome architecture as determined by genome-wide chromosome conformation capture (HiC) and described by the proposed fractal-globule model. This analysis suggests that the distribution of chromosomal alterations in cancer is spatially related to three-dimensional genomic architecture and that purifying selection, as well as positive selection, influences SCNAs during somatic evolution of cancer cells.     

Dynamic origin of spatially discordant alternans in cardiac tissue

Alternans, a condition in which there is a beat-to-beat alternation in the electromechanical response of a periodically stimulated cardiac cell, has been linked to the genesis of life-threatening ventricular arrhythmias. Optical mapping of membrane voltage (V_m) and intracellular calcium (Ca_i) on the surface of animal hearts reveals complex spatial patterns of alternans. In particular, spatially discordant alternans has been observed in which regions with a large-small-large action potential duration (APD) alternate out-of-phase adjacent to regions of small-large-small APD. However, the underlying mechanisms that lead to the initiation of discordant alternans and govern its spatiotemporal properties are not well understood. Using mathematical modeling, we show that dynamic changes in the spatial distribution of discordant alternans can be used to pinpoint the underlying mechanisms. Optical mapping of V_m and Ca_i in paced rabbit hearts revealed that spatially discordant alternans induced by rapid pacing exhibits properties consistent with a purely dynamical mechanism as shown in theoretical studies. Our results support the viewpoint that spatially discordant alternans in the heart can be formed via a dynamical pattern formation process which does not require tissue heterogeneity.     

Three-Dimensional Histology Volume Reconstruction of Axonal Tract Tracing Data: Exploring Topographical Organization in Subcortical Projections from Rat Barrel Cortex

Topographical organization is a hallmark of the mammalian brain, and the spatial organization of axonal connections in different brain regions provides a structural framework accommodating specific patterns of neural activity. The presence, amount, and spatial distribution of axonal connections are typically studied in tract tracing experiments in which axons or neurons are labeled and examined in histological sections. Three-dimensional (3-D) reconstruction techniques are used to achieve more complete visualization and improved understanding of complex topographical relationships. 3-D reconstruction approaches based on manually or semi-automatically recorded spatial points representing axonal labeling have been successfully applied for investigation of smaller brain regions, but are not practically feasible for whole-brain analysis of multiple regions. We here reconstruct serial histological images from four whole brains (originally acquired for conventional microscopic analysis) into volumetric images that are spatially registered to a 3-D atlas template. The aims were firstly to evaluate the quality of the 3-D reconstructions and the usefulness of the approach, and secondly to investigate axonal projection patterns and topographical organization in rat corticostriatal and corticothalamic pathways. We demonstrate that even with the limitations of the original routine histological material, the 3-D reconstructed volumetric images allow efficient visualization of tracer injection sites and axonal labeling, facilitating detection of spatial distributions and across-case comparisons. Our results further show that clusters of S1 corticostriatal and corticothalamic projections are distributed within narrow, elongated or spherical subspaces extending across the entire striatum / thalamus. We conclude that histology volume reconstructions facilitate mapping of spatial distribution patterns and topographical organization. The reconstructed image volumes are shared via the Rodent Brain Workbench (www.rbwb.org).     

Technical note: The use of geographical information systems software for the spatial analysis of bone microstructure

Geographic information systems (GIS) software is typically used for analyzing geographically distributed data, allowing users to annotate points or areas on a map and attach data for spatial analyses. While traditional GIS-based research involves geo-referenced data (points tied to geographic locations), the use of this technology has other constructive applications for physical anthropologists. The use of GIS software for the study of bone histology offers a novel opportunity to analyze the distribution of bone nano- and microstructures, relative to macrostructure and in comparison to other variables of interest, such as biomechanical loading history. This approach allows for the examination of characteristics of single histological features while considering their role at the macroscopic level. Such research has immediate promise in examining the load history of bone by surveying the functional relationship between collagen fiber orientation (CFO) and strain mode. The diversity of GIS applications that may be utilized in bone histology research is just beginning to be explored. The goal of this study is to introduce a reliable methodology for such investigation and our objective is to quantify the heterogeneity of bone microstructure over an entire cross-section of bone using ArcGIS v 9.3 (ESRI). This was accomplished by identifying the distribution of remodeling units in a human metatarsal relative to bending axes. One biomechanical hypothesis suggests that CFO, manifested by patterns of birefringence, is indicative of mode of strain during formation. This study demonstrates that GIS can be used to investigate, describe, and compare such patterns through histological mapping.     

Hierarchical band-target entropy minimization curve resolution and Pearson VII curve-fitting analysis of cellular protein infrared imaging spectra

A soft-modeling multivariate numerical approach that combines self-modeling curve resolution (SMCR) and mixed Lorentzian-Gaussian curve fitting was successfully implemented for the first time to elucidate spatially and spectroscopically resolved spectral information from infrared imaging data of oral mucosa cells. A novel variant form of the robust band-target entropy minimization (BTEM) SMCR technique, coined as hierarchical BTEM (hBTEM), was introduced to first cluster similar cellular infrared spectra using the unsupervised hierarchical leader-follower cluster analysis (LFCA) and subsequently apply BTEM to clustered subsets of data to reconstruct three protein secondary structure (PSS) pure component spectra—α-helix, β-sheet, and ambiguous structures—that associate with spatially differentiated regions of the cell infrared image. The Pearson VII curve-fitting procedure, which approximates a mixed Lorentzian-Gaussian model for spectral band shape, was used to optimally curve fit the resolved amide I and II bands of various hBTEM reconstructed PSS pure component spectra. The optimized Pearson VII band-shape parameters and peak center positions serve as means to characterize amide bands of PSS spectra found in various cell locations and for approximating their actual amide I/II intensity ratios. The new hBTEM methodology can also be potentially applied to vibrational spectroscopic datasets with dynamic or spatial variations arising from chemical reactions, physical perturbations, pathological states, and the like.     

The Evolution of Tumour Composition During Fractionated Radiotherapy: Implications for Outcome

Current protocols for delivering radiotherapy are based primarily on tumour stage and nodal and metastases status, even though it is well known that tumours and their microenvironments are highly heterogeneous. It is well established that the local oxygen tension plays an important role in radiation-induced cell death, with hypoxic tumour regions responding poorly to irradiation. Therefore, to improve radiation response, it is important to understand more fully the spatiotemporal distribution of oxygen within a growing tumour before and during fractionated radiation. To this end, we have extended a spatially resolved mathematical model of tumour growth, first proposed by Greenspan (Stud Appl Math 51:317–340, 1972), to investigate the effects of oxygen heterogeneity on radiation-induced cell death. In more detail, cell death due to radiation at each location in the tumour, as determined by the well-known linear-quadratic model, is assumed also to depend on the local oxygen concentration. The oxygen concentration is governed by a reaction-diffusion equation that is coupled to an integro-differential equation that determines the size of the assumed spherically symmetric tumour. We combine numerical and analytical techniques to investigate radiation response of tumours with different intratumoral oxygen distribution profiles. Model simulations reveal a rapid transient increase in hypoxia upon regrowth of the tumour spheroid post-irradiation. We investigate the response to different radiation fractionation schedules and identify a tumour-specific relationship between inter-fraction time and dose per fraction to achieve cure. The rich dynamics exhibited by the model suggest that spatial heterogeneity may be important for predicting tumour response to radiotherapy for clinical applications.     

Label-free 3D visualization of cellular and tissue structures in intact muscle with second and third harmonic generation microscopy

Second and Third Harmonic Generation (SHG and THG) microscopy is based on optical effects which are induced by specific inherent physical properties of a specimen. As a multi-photon laser scanning approach which is not based on fluorescence it combines the advantages of a label-free technique with restriction of signal generation to the focal plane, thus allowing high resolution 3D reconstruction of image volumes without out-of-focus background several hundred micrometers deep into the tissue. While in mammalian soft tissues SHG is mostly restricted to collagen fibers and striated muscle myosin, THG is induced at a large variety of structures, since it is generated at interfaces such as refraction index changes within the focal volume of the excitation laser. Besides, colorants such as hemoglobin can cause resonance enhancement, leading to intense THG signals. We applied SHG and THG microscopy to murine (Mus musculus) muscles, an established model system for physiological research, to investigate their potential for label-free tissue imaging. In addition to collagen fibers and muscle fiber substructure, THG allowed us to visualize blood vessel walls and erythrocytes as well as white blood cells adhering to vessel walls, residing in or moving through the extravascular tissue. Moreover peripheral nerve fibers could be clearly identified. Structure down to the nuclear chromatin distribution was visualized in 3D and with more detail than obtainable by bright field microscopy. To our knowledge, most of these objects have not been visualized previously by THG or any label-free 3D approach. THG allows label-free microscopy with inherent optical sectioning and therefore may offer similar improvements compared to bright field microscopy as does confocal laser scanning microscopy compared to conventional fluorescence microscopy.     

A ratiometric imaging method for mapping ion flux densities

A heterogeneous distribution of ion channels on the cell surface is a prerequisite for several cellular functions. Thus, there has been considerable interest in methods allowing the mapping of ion channel distributions. Here we report on a novel ratiometric imaging technique appropriate to measure spatially resolved ion flux signals by using ion sensitive dyes. However, given that certain relevant cell properties like the surface to volume ratio may exhibit significant spatial heterogeneities, the local influx signal cannot be interpreted as a measure of the local open channel concentration or flux density. To overcome this problem, we suggest an internal normalization procedure, which, in analogy to, but clearly distinct from, well-established ratioing techniques, eliminates effects which would otherwise obscure the desired result. Ratioing is performed on flux signals from a given cell, triggered by two different, subsequent stimuli. If the two stimuli address different ion channels, the flux density distribution caused by two channel types can be determined relative to each other. In cases where one of the stimuli triggers a spatially homogeneous flux signal, ratioing yields an ion flux density map for a given channel type. Thus distribution patterns of ion channels active during a given stimulus may be derived.     

Multispectral near infrared absorption imaging for histology of skin cancer

Multispectral imaging combines the spectral resolution of spectroscopy with the spatial resolution of imaging and is therefore very useful for biomedical applications. Currently, histological diagnostics use mainly stainings with standard dyes (eg, hematoxylin + eosin) to identify tumors. This method is not applicable in vivo and provides low amounts of chemical information. Biomolecules absorb near infrared light (NIR, 800‐1700 nm) at different wavelengths, which could be used to fingerprint tissue. Here, we built a NIR multispectral absorption imaging setup to study skin tissue samples. NIR light (900‐1500 nm) was used for homogenous wide‐field transmission illumination and detected by a cooled InGaAs camera. In this setup, images I(x, y, λ) from dermatological samples (melanoma, nodular basal‐cell carcinoma, squamous‐cell carcinoma) were acquired to distinguish healthy from diseased tissue regions. In summary, we show the potential of multispectral NIR imaging for cancer diagnostics.      

The importance of environmental spatial heterogeneity and host social structure in disease transmission

[First paragraph]The spatial structure of a host population determines the spatial probability distribution of interaction between individuals, and therefore influences the spatio-temporal dynamics of disease transmission within the host population (Keeling, 1999; Gudelj and White, 2004). Nigel Barlow recognised this and included non-linear transmission in his later models (Barlow, 1991), simulating the result of spatial heterogeneity of risk in susceptible hosts. These models produced behaviour that could not be found in models with homogeneously mixed host populations: more rapid disease dynamics and a greater robustness of disease to control measures. However, in this model there was no causal mechanism driving the initial spatial heterogeneity of risk in host individuals. Environmental heterogeneity is likely to be a key factor in determining the spatial distribution of host individuals (Cronin and Reeve, 2005). We attempted to explore how environmental heterogeneity may affect disease dynamics via its influence on the spatial distribution of host individuals. We developed a spatially explicit stochastic model that incorporated spatially variable host density distributions, primarily driven by environmental heterogeneity.     

DNA replication timing and long-range DNA interactions predict mutational landscapes of cancer genomes

Somatic copy-number alterations (SCNA) are a hallmark of many cancer types, but the mechanistic basis underlying their genome-wide patterns remains incompletely understood. Here we integrate data on DNA replication timing, long-range interactions between genomic material, and 331,724 SCNAs from 2,792 cancer samples classified into 26 cancer types. We report that genomic regions of similar replication timing are clustered spatially in the nucleus, that the two boundaries of SCNAs tend to be found in such regions, and that regions replicated early and late display distinct patterns of frequencies of SCNA boundaries, SCNA size and a preference for deletions over insertions. We show that long-range interaction and replication timing data alone can identify a significant proportion of SCNAs in an independent test data set. We propose a model for the generation of SCNAs in cancer, suggesting that data on spatial proximity of regions replicating at the same time can be used to predict the mutational landscapes of cancer genomes.