Single-Cell Screening of Tamoxifen Abundance and Effect Using Mass Spectrometry and Raman-Spectroscopy

Enabling high-sensitivity live single-cell mass spectrometry using an integrated electrical lysis and nano electrospray ionization interface

BACKGROUND

Live single-cell metabolomic studies encounter inherent difficulties attributed to the limited sample volume, minimal compound quantity, and insufficient sensitivity in the Mass Spectrometry (MS) method used to obtain single-cell data. However, understanding cellular heterogeneity, functional diversity, and metabolic processes within individual cells is essential. Exploring how individual cells respond to stimuli, including drugs, environmental changes, or signaling molecules, offers insights into biology, oncology, and drug discovery. Efficient release of cell contents (lysis) is vital for accurate metabolite detection at the single-cell level. Despite this, traditional approaches in live single cell metabolomics methods do not emphasize efficient lysis to prevent sample dilution. Instead, current live single cell metabolomics methods use direct infusion to introduce the cell into the mass spectrometry without prior chromatographic separation or a lysis step, which adversely affects sensitivity and metabolic coverage.

RESULTS

To address this, we developed an integrated single-cell electrical lysis and nano spray (SCEL-nS) platform coupled to an Orbitrap MS capable of efficiently lysing a single cell after being sampled with specially manufactured micropipettes. Lysis efficiency was validated by comparing live cell stain fluorescent intensities of intact and electrically lysed cells through microscopy imaging. The SCEL-nS platform successfully induced the breakdown of a single cell, significantly reducing the live cell stain's fluorescent intensity indicating cell membrane breakdown. Additionally, SCEL-nS was validated by measuring single cells spiked with the anti-cancer drug tamoxifen by MS. SCEL-nS use resulted in statistically significant increase in the peak measured by the method compared to the traditional non-lysis method.

SIGNIFICANCE

Overall, our results demonstrate that the newly incorporated SCEL-nS platform achieved higher sensitivities compared to traditional live single cell analysis methods.

Raman spectroscopic deep learning with signal aggregated representations for enhanced cell phenotype and signature identification

Feature representation is critical for data learning, particularly in learning spectroscopic data. Machine learning (ML) and deep learning (DL) models learn Raman spectra for rapid, nondestructive, and label-free cell phenotype identification, which facilitate diagnostic, therapeutic, forensic, and microbiological applications. But these are challenged by high-dimensional, unordered, and low-sample spectroscopic data. Here, we introduced novel 2D image-like dual signal and component aggregated representations by restructuring Raman spectra and principal components, which enables spectroscopic DL for enhanced cell phenotype and signature identification. New ConvNet models DSCARNets significantly outperformed the state-of-the-art (SOTA) ML and DL models on six benchmark datasets, mostly with >2% improvement over the SOTA performance of 85-97% accuracies. DSCARNets also performed well on four additional datasets against SOTA models of extremely high performances (>98%) and two datasets without a published supervised phenotype classification model. Explainable DSCARNets identified Raman signatures consistent with experimental indications.

Unraveling the Drug Response Heterogeneity with Single-Cell Vibrational Phenomics

Drug responses heterogeneity is often highlighted to justify the need for precision medicine. However, due to the highly complex nature of cell phenotypes in many diseases, one of key challenges is how to obtain the high content features in a cellular population. Here we present a single-cell vibrational phenomics approach, integrating synchrotron infrared microspectroscopy and multivariate calculation, for quantitatively evaluating the cellular responses to drug perturbation with single cell resolution. In a human hepatocellular carcinoma HepG2 cell model, the phenotypic changes induced by two types of drugs, taxol (TAX) and protopanaxadiol (PPD), were analyzed and revealed the response heterogeneity in drug concentration and chemical components. These findings not only provide a label-free strategy for determining the drug response at the single cell level, but also demonstrate the great potential of vibrational phenomics as a drug discovery platform.

Spectral insights: Navigating the frontiers of biomedical and microbiological exploration with Raman spectroscopy

Raman spectroscopy (RS), a powerful analytical technique, has gained increasing recognition and utility in the fields of biomedical and biological research. Raman spectroscopic analyses find extensive application in the field of medicine and are employed for intricate research endeavors and diagnostic purposes. Consequently, it enjoys broad utilization within the realm of biological research, facilitating the identification of cellular classifications, metabolite profiling within the cellular milieu, and the assessment of pigment constituents within microalgae. This article also explores the multifaceted role of RS in these domains, highlighting its distinct advantages, acknowledging its limitations, and proposing strategies for enhancement.

Establishment of a Method for the Introduction of Poorly Water-Soluble Drugs in Cells and Evaluation of Intracellular Concentration Distribution Using Resonance Raman Imaging

Label-free measurement is essential to understand the metabolism of drug molecules introduced into cells. Raman imaging is a powerful method to investigate intracellular drug molecules because it provides in situ label-free observation of introduced molecules. In this study, we propose that Raman imaging can be used not only to observe the intracellular distribution of drug molecules but also to quantitatively visualize the concentration distribution reflecting each organelle in a single living cell using the Raman band of extracellular water as an intensity standard. We dissolved poorly water-soluble all-trans-retinoic acid (ATRA) in water using a cytocompatible amphiphilic phospholipid polymer, poly[2-methacryloyloxyethyl phosphorylcholine-co-n-butyl methacrylate] (PMB) as a solubilizing reagent, introduced it into cells, and obtained the intracellular concentration distribution of ATRA. ATRA was concentrated in the cells and mainly localized to mitochondria and lipid droplets, interacting strongly with mitochondria and weakly with lipid droplets. Poorly water-soluble β-carotene was also introduced into cells using PMB but was not concentrated intracellularly, indicating that β-carotene does not interact specifically with intracellular molecules. We established a protocol for the solubilization and intracellular uptake of poorly water-soluble molecules using PMB and obtaining their concentration distribution using Raman microscopy.

Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting

Biomolecular abundance detection of fermentation microorganisms is significant for the accurate regulation of fermentation, which is conducive to reducing fermentation costs and improving the yield of target products. However, the development of an accurate analytical method for the detection of biomolecular abundance still faces important challenges. Herein, we present a non-invasive biomolecular abundance detection method based on Raman spectra combined with target extraction and multimodel fitting. The high gain of the eXtreme Gradient Boosting (XGBoost) algorithm was used to extract the characteristic Raman peaks of metabolically active proteins and nucleic acids within E. coli and yeast. The test accuracy for different culture times and cell cycles of E. coli was 94.4% and 98.2%, respectively. Simultaneously, the Gaussian multi-peak fitting algorithm was exploited to calculate peak intensity from mixed peaks, which can improve the accuracy of biomolecular abundance calculations. The accuracy of Gaussian multi-peak fitting was above 0.9, and the results of the analysis of variance (ANOVA) measurements for the lag phase, log phase, and stationary phase of E. coli growth demonstrated highly significant levels, indicating that the intracellular biomolecular abundance detection was consistent with the classical cell growth law. These results suggest the great potential of the combination of microbial intracellular abundance, Raman spectra analysis, target extraction, and multimodel fitting as a method for microbial fermentation engineering.

Next-generation pathology practices with mass spectrometry imaging

Mass spectrometry imaging (MSI) is a powerful technique that reveals the spatial distribution of various molecules in biological samples, and it is widely used in pathology-related research. In this review, we summarize common MSI techniques, including matrix-assisted laser desorption/ionization and desorption electrospray ionization MSI, and their applications in pathological research, including disease diagnosis, microbiology, and drug discovery. We also describe the improvements of MSI, focusing on the accumulation of imaging data sets, expansion of chemical coverage, and identification of biological significant molecules, that have prompted the evolution of MSI to meet the requirements of pathology practices. Overall, this review details the applications and improvements of MSI techniques, demonstrating the potential of integrating MSI techniques into next-generation pathology practices.

Monitoring Metabolic Changes in Response to Chemotherapies in Cancer with Raman Spectroscopy and Metabolomics

Resistance to clinical therapies remains a major barrier in cancer management. There is a critical need for rapid and highly sensitive diagnostic tools that enable early prediction of treatment response to allow accurate clinical decisions. Here, Raman spectroscopy was employed to monitor changes in key metabolites as early predictors of response in KRAS-mutant colorectal cancer (CRC) cells, HCT116, treated with chemotherapies. We show at the single cell level that HCT116 is resistant to cetuximab (CTX), the first-line treatment in CRC, but this resistance can be overcome with pre-sensitization of cells with oxaliplatin (OX). In combination treatment of CTX + OX, sequential delivery of OX followed by CTX rather than simultaneous administration of drugs was observed to be critical for effective therapy. Our results demonstrated that metabolic changes are well aligned to cellular mechanical changes where Young's modulus decreased after effective treatment, indicating that both changes in mechanical properties and metabolism in cells are likely responsible for cancer proliferation. Raman findings were verified with mass spectrometry (MS) metabolomics, and both platforms showed changes in lipids, nucleic acids, and amino acids as predictors of resistance/response. Finally, key metabolic pathways enriched were identified when cells are resistant to CTX but downregulated with effective treatment. This study highlights that drug-induced metabolic changes both at the single cell level (Raman) and ensemble level (MS) have the potential to identify mechanisms of response to clinical cancer therapies.

Spatial Transcriptomic Technologies

Spatial transcriptomic technologies enable measurement of expression levels of genes systematically throughout tissue space, deepening our understanding of cellular organizations and interactions within tissues as well as illuminating biological insights in neuroscience, developmental biology and a range of diseases, including cancer. A variety of spatial technologies have been developed and/or commercialized, differing in spatial resolution, sensitivity, multiplexing capability, throughput and coverage. In this paper, we review key enabling spatial transcriptomic technologies and their applications as well as the perspective of the techniques and new emerging technologies that are developed to address current limitations of spatial methodologies. In addition, we describe how spatial transcriptomics data can be integrated with other omics modalities, complementing other methods in deciphering cellar interactions and phenotypes within tissues as well as providing novel insight into tissue organization.

Single-cell metabolomics by mass spectrometry: ready for primetime?

Single-cell metabolomics (SCMs) is a powerful tool for studying cellular heterogeneity by providing insight into the differences between individual cells. With the development of a set of promising SCMs pipelines, this maturing technology is expected to be widely used in biomedical research. However, before SCMs is ready for primetime, there are some challenges to overcome. In this review, we summarize the trends and challenges in the development of SCMs. We also highlight the latest methodologies, applications, and sketch the perspective for integration with other omics and imaging approaches.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

Nanocapillary sampling coupled to liquid chromatography mass spectrometry delivers single cell drug measurement and lipid fingerprints

This work describes the development of a new approach to measure drug levels and lipid fingerprints in single living mammalian cells. Nanocapillary sampling is an approach that enables the selection and isolation of single living cells under microscope observation. Here, live single cell nanocapillary sampling is coupled to liquid chromatography for the first time. This allows molecular species to be separated prior to ionisation and improves measurement precision of drug analytes. The efficiency of transferring analytes from the sampling capillary into a vial was optimised in this work. The analysis was carried out using standard flow liquid chromatography coupled to widely available mass spectrometry instrumentation, highlighting opportunities for widespread adoption. The method was applied to 30 living cells, revealing cell-to-cell heterogeneity in the uptake of different drug molecules. Using this system, we detected 14-158 lipid features per single cell, revealing the association between bedaquiline uptake and lipid fingerprints.

Single-cell mass spectrometry

Owing to recent advances in mass spectrometry (MS), tens to hundreds of proteins, lipids, and small molecules can be measured in single cells. The ability to characterize the molecular heterogeneity of individual cells is necessary to define the full assortment of cell subtypes and identify their function. We review single-cell MS including high-throughput, targeted, mass cytometry-based approaches and antibody-free methods for broad profiling of the proteome and metabolome of single cells. The advantages and disadvantages of different methods are discussed, as well as the challenges and opportunities for further improvements in single-cell MS. These methods is being used in biomedicine in several applications including revealing tumor heterogeneity and high-content drug screening.

Advances in measuring cancer cell metabolism with subcellular resolution

Characterizing metabolism in cancer is crucial for understanding tumor biology and for developing potential therapies. Although most metabolic investigations analyze averaged metabolite levels from all cell compartments, subcellular metabolomics can provide more detailed insight into the biochemical processes associated with the disease. Methodological limitations have historically prevented the wider application of subcellular metabolomics in cancer research. Recently, however, ways to distinguish and identify metabolic pathways within organelles have been developed, including state-of-the-art methods to monitor metabolism in situ (such as mass spectrometry-based imaging, Raman spectroscopy and fluorescence microscopy), to isolate key organelles via new approaches and to use tailored isotope-tracing strategies. Herein, we examine the advantages and limitations of these developments and look to the future of this field of research.

Tracking metabolites at single-cell resolution reveals metabolic dynamics during plant mitosis

Analyzing only one cell allows the changes and characteristics of intracellular metabolites during the chromosome segregation process to be precisely captured and mitotic sub-phases to be dissected at the metabolite level.

Capillary microsampling-based single-cell metabolomics by mass spectrometry and its applications in medicine and drug discovery

Characterization of cellular metabolic states is a technical challenge in biomedicine. Cellular heterogeneity caused by inherent diversity in expression of metabolic enzymes or due to sensitivity of metabolic reactions to perturbations, necessitates single cell analysis of metabolism. Heterogeneity is typically seen in cancer and thus, single-cell metabolomics is expectedly useful in studying cancer progression, metastasis, and variations in cancer drug response. However, low sample volumes and analyte concentrations limit detection of critically important metabolites. Capillary microsampling-based mass spectrometry approaches are emerging as a promising solution for achieving single-cell omics. Herein, we focus on the recent advances in capillary microsampling-based mass spectrometry techniques for single-cell metabolomics. We discuss recent technical developments and applications to cancer medicine and drug discovery.

Liquid-Liquid Phase Separation in Cancer Signaling, Metabolism and Anticancer Therapy

The cancer state is thought to be maintained by genetic and epigenetic changes that drive a cancer-promoting gene expression program. However, recent results show that cellular states can be also stably maintained by the reorganization of cell structure leading to the formation of biological condensates via the process of liquid-liquid phase separation. Here, we review the data showing cancer-specific biological condensates initiated by mutant oncoproteins, RNA-binding proteins, or lincRNAs that regulate oncogenic gene expression programs and cancer metabolism. Effective anticancer drugs may specifically partition into oncogenic biological condensates (OBC).

Analysis of the intracellular localization of amiodarone using live single-cell mass spectrometry

Amiodarone is a well-known antiarrhythmic drug with side effects including phospholipidosis. However, it is not clear how amiodarone and its metabolites are localized in the cell. In the present study, the localization of amiodarone in the cytosol, vacuoles, and lipid droplets of a single HepG2 human hepatocellular carcinoma cell was determined directly using live single-cell mass spectrometry. The cytosol, vacuoles, and lipid droplets of a single HepG2 cell treated with amiodarone were separately captured using a nano-spray tip under a fluorescence microscope after visualizing the lipid droplets using a fluorescent probe. This assay showed a linearity in the measurement of amiodarone levels with R² values of 0.9996 and 0.9998 in the cell lysates and serum, respectively. The peak intensities of amiodarone and its metabolites in lipid droplets and vacuoles were significantly higher than those in the cytosol, while those in lipid droplets were higher than those in vacuoles. Amiodarone metabolites were detected in both lipid droplets and the cytosol. Live single-cell mass spectrometry combined with fluorescence imaging demonstrated clear localization of amiodarone and its metabolites in lipid droplets separately from the vacuole. This assay system combined with fluorescence imaging could be useful for investigating the intracellular localization of various drugs and their metabolites.

Spatially Resolved Mass Spectrometry at the Single Cell: Recent Innovations in Proteomics and Metabolomics

Biological systems are composed of heterogeneous populations of cells that intercommunicate to form a functional living tissue. Biological function varies greatly across populations of cells, as each single cell has a unique transcriptome, proteome, and metabolome that translates to functional differences within single species and across kingdoms. Over the past decade, substantial advancements in our ability to characterize omic profiles on a single cell level have occurred, including in multiple spectroscopic and mass spectrometry (MS)-based techniques. Of these technologies, spatially resolved mass spectrometry approaches, including mass spectrometry imaging (MSI), have shown the most progress for single cell proteomics and metabolomics. For example, reporter-based methods using heavy metal tags have allowed for targeted MS investigation of the proteome at the subcellular level, and development of technologies such as laser ablation electrospray ionization mass spectrometry (LAESI-MS) now mean that dynamic metabolomics can be performed in situ. In this Perspective, we showcase advancements in single cell spatial metabolomics and proteomics over the past decade and highlight important aspects related to high-throughput screening, data analysis, and more which are vital to the success of achieving proteomic and metabolomic profiling at the single cell scale. Finally, using this broad literature summary, we provide a perspective on how the next decade may unfold in the area of single cell MS-based proteomics and metabolomics.

Analysis of Live Single Cells by Confocal Microscopy and High-Resolution Mass Spectrometry to Study Drug Uptake, Metabolism, and Drug-Induced Phospholipidosis

The analysis of large numbers of cells from a population results in information that does not reflect differences in cell phenotypes. Individual variations in cellular drug uptake, metabolism, and response to drug treatment may have profound effects on cellular survival and lead to the development of certain disease states, drug persistence, and resistance. Herein, we present a method that combines live cell confocal microscopy imaging with high-resolution mass spectrometry to achieve absolute cell quantification of the drug amiodarone (AMIO) and its major metabolite, N-desethylamiodarone (NDEA), in single liver cells (HepG2 and HepaRG cells). The method uses a prototype system that integrates a confocal microscope with an XYZ stage robot to image and automatically sample selected cells from a sample compartment, which is kept under growth conditions, with nanospray tips. Besides obtaining the distributions of AMIO and NDEA cell concentrations across a population of individual cells, as well as variabilities in drug metabolism, the effect of these on phospholipidosis and cell morphology was studied. The method was suited to identify subpopulations of cells that metabolized less drug and to correlate cell drug concentrations with cell phospholipid content, cell volume, sphericity, and other cell phenotypic features. Using principal component analysis (PCA), the treated cells could be clearly distinguished from vehicle control cells (0 μM AMIO) and HepaRG cells from HepG2 cells. The potential of using multidimensional and multimodal information collected from single cells to build predictive models for cell classification is demonstrated.

Multimodal Imaging Mass Spectrometry: Next Generation Molecular Mapping in Biology and Medicine

Imaging mass spectrometry has become a mature molecular mapping technology that is used for molecular discovery in many medical and biological systems. While powerful by itself, imaging mass spectrometry can be complemented by the addition of other orthogonal, chemically informative imaging technologies to maximize the information gained from a single experiment and enable deeper understanding of biological processes. Within this review, we describe MALDI, SIMS, and DESI imaging mass spectrometric technologies and how these have been integrated with other analytical modalities such as microscopy, transcriptomics, spectroscopy, and electrochemistry in a field termed multimodal imaging. We explore the future of this field and discuss forthcoming developments that will bring new insights to help unravel the molecular complexities of biological systems, from single cells to functional tissue structures and organs.

Raman Microscopy: Progress in Research on Cancer Cell Sensing

In the last decade, Raman Spectroscopy (RS) was demonstrated to be a label-free, non-invasive and non-destructive optical spectroscopy allowing the improvement in diagnostic accuracy in cancer and analytical assessment for cell sensing. This review discusses how Raman spectra can lead to a deeper molecular understanding of the biochemical changes in cancer cells in comparison to non-cancer cells, analyzing two key examples, leukemia and breast cancer. The reported Raman results provide information on cancer progression and allow the identification, classification, and follow-up after chemotherapy treatments of the cancer cells from the liquid biopsy. The key obstacles for RS applications in cancer cell diagnosis, including quality, objectivity, number of cells and velocity of the analysis, are considered. The use of multivariant analysis, such as principal component analysis (PCA) and linear discriminate analysis (LDA), for an automatic and objective assessment without any specialized knowledge of spectroscopy is presented. Raman imaging for cancer cell mapping is shown and its advantages for routine clinical pathology practice and live cell imaging, compared to single-point spectral analysis, are debated. Additionally, the combination of RS with microfluidic devices and high-throughput screening for improving the velocity and the number of cells analyzed are also discussed. Finally, the combination of the Raman microscopy (RM) with other imaging modalities, for complete visualization and characterization of the cells, is described.

Approaching sites of action of drugs in clinical pharmacology: New analytical options and their challenges

Clinical pharmacology is an important discipline for drug development aiming to define pharmacokinetics (PK), pharmacodynamics (PD) and optimum exposure to drugs, i.e. the concentration-response relationship and its modulators. For this purpose, information on drug concentrations at the anatomical, cellular and molecular sites of action is particularly valuable. In pharmacological assays, the limited accessibility of target cells in readily available samples (i.e. blood) often hampers mass spectrometry-based monitoring of the absolute quantity of a compound and the determination of its molecular action at the cellular level. Recently, new sample collection methods have been developed for the specific capture of rare circulating cells, especially for the diagnosis of circulating tumour cells. In parallel, new advances and developments in mass spectrometric instrumentation now allow analyses to be scaled down to the cellular level. Together, these developments may permit the monitoring of minute drug quantities and show their effect at the cellular level. In turn, such PK/PD associations on a cellular level would not only enrich our pharmacological knowledge of a given compound but also expand the basis for PK/PD simulations. In this review, we describe novel concepts supporting clinical pharmacology at the anatomical, cellular and molecular sites of action, and highlight the new challenges in mass spectrometry-based monitoring. Moreover, we present methods to tackle these challenges and define future needs.

Mechanistic and Quantitative Understanding of Pharmacokinetics in Zebrafish Larvae through Nanoscale Blood Sampling and Metabolite Modeling of Paracetamol

Zebrafish larvae are increasingly used for pharmacological research, but internal drug exposure is often not measured. Understanding pharmacokinetics is necessary for reliable translation of pharmacological results to higher vertebrates, including humans. Quantification of drug clearance and distribution requires measurements of blood concentrations. Additionally, measuring drug metabolites is of importance to understand clearance in this model organism mechanistically. We therefore mechanistically studied and quantified pharmacokinetics in zebrafish larvae, and compared this to higher vertebrates, using paracetamol (acetaminophen) as a paradigm compound. A method was developed to sample blood from zebrafish larvae 5 days post fertilization. Blood concentrations of paracetamol and its major metabolites, paracetamol-glucuronide and paracetamol-sulfate, were measured. Blood concentration data were combined with measured amounts in larval homogenates and excreted amounts and simultaneously analyzed through nonlinear mixed-effects modeling, quantifying absolute clearance and distribution volume. Blood sampling from zebrafish larvae was most successful from the posterior cardinal vein, with a median volume (interquartile range) of 1.12 nl (0.676-1.66 nl) per blood sample. Samples were pooled (n = 15-35) to reach measurable levels. Paracetamol blood concentrations at steady state were only 10% of the external paracetamol concentration. Paracetamol-sulfate was the major metabolite, and its formation was quantified using a time-dependent metabolic formation rate. Absolute clearance and distribution volume correlated well with reported values in higher vertebrates, including humans. Based on blood concentrations and advanced data analysis, the mechanistic and quantitative understanding of paracetamol pharmacokinetics in zebrafish larvae has been established. This will improve the translational value of this vertebrate model organism in drug discovery and development. SIGNIFICANCE STATEMENT: In early phases of drug development, new compounds are increasingly screened in zebrafish larvae, but the internal drug exposure is often not taken into consideration. We developed innovative experimental and computational methods, including a blood-sampling technique, to measure the paradigm drug paracetamol (acetaminophen) and its major metabolites and quantify pharmacokinetics (absorption, distribution, elimination) in zebrafish larvae of 5 days post fertilization with a total volume of only 300 nl. These parameter values were scaled to higher vertebrates, including humans.