Open Access
4 September 2020 Photoacoustics resolves species-specific differences in hemoglobin concentration and oxygenation
Lina Hacker, Joanna Brunker, Ewan S. Smith, Isabel Quirós-Gonzalez, Sarah E. Bohndiek
Author Affiliations +
Abstract

Significance: Photoacoustic imaging (PAI) enables the detection of blood hemoglobin (HB) concentration and oxygenation (sO2) with high contrast and resolution. Despite the heavy use of photoacoustically determined total hemoglobin (THb) and oxygenation (sO2) biomarkers in PAI research, their relationship with underlying biochemical blood parameters and the impact of intra- and interspecies genetic variability have yet to be established.

Aim: To explore the relationship between THb and sO2 photoacoustic biomarkers and the underlying biochemical blood parameters in a species-specific manner.

Approach: Experiments were performed on blood in vitro using tissue-mimicking agar phantoms. Blood was extracted from mouse, rat, human, and naked mole-rat (Heterocephalus glaber), anticoagulated in ethylenediaminetetraacetic acid, and measured within 48 h. THb and sO2 were measured using a commercial photoacoustic tomography system (InVision 128, iThera Medical GmBH). Biochemical blood parameters such as HB concentration (g/dL), hematocrit (HCT, %), and red blood cell (RBC) count (μL  −  1) were assessed using a hematology analyzer (Mythic 18 Vet, Woodley Equipment).

Results: A significant correlation was observed between THb and biochemical HB, HCT, and RBC in mouse and rat blood. Moreover, PAI accurately recapitulated interspecies variations in HB and HCT between mouse and rat blood and resolved differences in the oxygen dissociation curves measured using sO2 between human, mouse, and rat. With these validation data in hand, we applied PAI to studies of blood obtained from naked mole-rats and could confirm the high oxygen affinity of this species in comparison to other rodents of similar size.

Conclusions: Our results demonstrate the high sensitivity of photoacoustically determined hemoglobin biomarkers toward species-specific variations in vitro.

1.

Introduction

Photoacoustic imaging (PAI) is an emerging modality able to reveal high image contrast, arising from optical absorption in tissue at high spatial resolution, afforded by ultrasound detection. PAI is based on the photoacoustic principle,1 whereby pulsed light is absorbed by chromophores resulting in the generation of pressure waves that can be detected by ultrasound transducers at the tissue surface. Applying PAI at multiple wavelengths enables noninvasive, label-free detection of total hemoglobin concentration (THb) and oxygenation (sO2) based on the different optical absorption spectra of deoxygenated (HbR) and oxygenated hemoglobin (HbO2).2 The relative concentrations of these respective chromophores can then be calculated by spectroscopic inversion.3 PAI measures of THb (HbR+HbO2) and sO2 (HbO2/THb) have been widely exploited to characterize tissues in the context of different pathologies, for example in breast cancer,46 melanomas,7,8 prostate cancer,911 nodal lesions of the head and neck,12 vascular diseases,1315 and colitis.16 Particularly in cancer biology, THb and sO2 have proved to be of high value by allowing the detection of two cancer hallmarks: angiogenesis and hypoxia.17

Despite their extensive use in PAI, the relationship between photoacoustically determined THb and sO2 biomarkers and the underlying physiological variations in biochemical blood parameters, such as hemoglobin (HB) concentration (g/dL), hematocrit (HCT, %), and red blood cell (RBC) count (μL1), has yet to be established. HB is an iron-containing hem tetramer composed of two α- and two β-monomers and responsible for oxygen transport in all vertebrates,18 yet HB-related blood parameters differ within and between species and human race,19,20 with age and sex2123 of the individual, and can change under pathophysiological conditions or pharmaceutical treatment.24 Moreover, genetic modifications of the HB protein sequence resulting from either accumulated evolutionary changes or spontaneous mutations can alter the oxygen binding capabilities of the HB molecule.2527 Such differences could lead to substantial variations in PAI biomarkers or, for example, signal dynamics during more complex PAI protocols such as a gas challenge,10 which could lead to misinterpretation of PAI data.

Here, we investigate the relationship of photoacoustically determined THb and sO2 biomarkers with biochemical blood parameters taken from mice, rats, and humans in a controlled tissue-mimicking phantom system. Having examined the intraspecies homogeneity of the globin genes, we first elucidate intra- and interspecies differences between photoacoustic THb and static biochemical blood parameters in mouse and rat. We then move on by examining the differences in the dynamics of the oxygen dissociation curves (ODCs), here including human as a further species. With these validation data in hand, we then apply PAI to studies of blood obtained from naked mole-rats (Heterocephalus glaber), a species remarkable for its resistance to oxygen deprivation.28 Our results confirm the high sensitivity of photoacoustically determined biomarkers toward species-specific variations in vitro, which should be considered in future study designs.

2.

Materials and Methods

2.1.

Sequencing

Deoxyribose nucleic acid (DNA) was isolated from liver samples of female C57BL/6J mice (3 to 4 months, n=3) and Wistar rats (6 to 9 months, n=3) (Charles River) using the Qiagen DNeasy Blood/Tissue kit (Cat. no. 69504) following the manufacturer’s instruction. Primers (displayed in Table 1) for both the forward and reverse strand of each gene were designed using Primer 3 software (version 4.0.0).29 Polymerase chain reaction (PCR) amplification was performed using the Q5® Hot Start High-Fidelity DNA Polymerase (New England BioLabs Inc.) and the corresponding protocol30 with an annealing temperature of 66°C. Products were purified and sequenced using Sanger sequencing (SourceBioscience). Sequences were aligned using Clustal Omega.31 The identity match between the sequences was calculated.

Table 1

Confirmed intraspecies gene sequence identity of HB α- and β-genes in female mouse (n=3) and rat (n=3).

SpeciesGenePrimer sequenceIntraspecies gene identity (%)
MouseHBA-A1Exon 1+2F: GGGCAACTGATAAGGATTCCC100
R: GACCACTATGTTCCCTGCCT
Exon 3F: TGTCCACTTTGTCTCCGCA100
R: ACATGACACCTTTGCAGACG
HBA-A2Exon 1+2F: CTACTTGCTGCAGGTCCAA100
R: CCAGGTCCCAGCGCATAC
Exon 3F: TGTCCACTTTGTCTCCGCA100
R: AGAAGCGTCCCCACACTAAA
HBB-tExon 1+2F: TCATCTCTGAAGCCTCACCC100
R: ATAGCCAGGGGAAGGAAACC
RatHBA-A1Exon 1+2F: GAAACTTGCTGCAGGGTCAA100
R: GCCAGGTCTGAGCTCACA
HBA-A2Exon 1+2F: CAATGACAGCTGCTCCAAGG100
R: CAAGGGATCTCTGGAGGACC
HBA-A3Exon 1+2F: GCTGCAGGGCCAATACATTC100
R: GCCAGGTCTGAGCTCACA
HBBExon 1+2F: ATTGGCCAATCTGCTCACAC100
R: GAAAGCCACAGGAAGGACAC

2.2.

Blood Samples

Whole blood and tissue samples (liver) for DNA extraction originating from female C57BL/6J mice (3 to 4 months) and Wistar rats (6 to 9 months) were ordered from Charles River Laboratories. For comparison of the blood parameters in Secs. 13, the same sex was chosen as HB parameters have been shown to be sensitive to the sex of individuals.22 For the blood oxygenation experiments in the remaining Secs. 4 and 5, blood from mixed sex was used to achieve more generalizable results. Human blood samples from healthy donors were collected under the research ethics approval of the Royal Papworth Hospital tissue bank (project number T02196) in Cambridge. Blood from naked-mole rats (15 to 23 months, all male) was collected as a postmortem nonregulated procedure following decapitation of the animal for another scientific purpose. All blood samples were anticoagulated in ethylenediaminetetraacetic acid, directly stored at 4°C, and processed within 48 h.

2.3.

Blood Analysis

Blood parameters were assessed using an impedance-based hematology analyzer (Mythic 18, Woodley Veterinary Diagnostics32). The parameters obtained included: absolute and relative number of lymphocytes, monocytes, and granulocytes; absolute number of RBC (μL1) and white blood cells (WBC, μL1); HB concentration (g/dL); mean corpuscular hemoglobin (MCH, pg); mean corpuscular volume (MCV, μm3); mean corpuscular hemoglobin concentration (MCHC, g/dL); and HCT (%). MCV, MCHC, and MCH are commonly defined as: MCV = [HCT]/RBC, MCHC = [HB]/[HCT], and MCH = [HB]/RBC, where square brackets denote concentrations.

2.4.

Absorption Spectra Measurement

The absorption profile of HB was independently verified. Whole blood was lysed in distilled water (1:1) and the HB extracted in order to avoid artifacts caused by light scattering of the erythrocytes. For HB extraction,33 blood was centrifuged three times at 3000 rpm for 3 min and washed with phosphate buffered saline (PBS) to remove the plasma. Afterward, 1 unit volume of blood was thoroughly mixed with 1 unit volume of deionized water and 0.4 unit volume of toluene. The mixture was stored at 4°C for at least 24 h to ensure complete hemolysis. The solution was then centrifuged at 13,000 rpm for 10 min. The lowest layer containing the HB was extracted by syringe and filtered through a syringe filter (Millex, Millipore) with a pore size of 0.22  μm to remove the cell debris and large particles. The absorption spectrum of extracted HB was recorded in the range of 600 to 900 nm using a microplate reader (Clariostar, BMG Labtech). The absorption spectra were normalized to the area under the curve to account for the different HB levels of rat and mouse blood.

2.5.

Phantom Preparation and Photoacoustic Imaging

Agar phantoms were produced according to the protocol by Joseph et al.36 Briefly, liquid 1.5% w/v agar (Fluka 05039) was mixed with 2.1% v/v prewarmed intralipid (Sigma-Aldrich I141) to provide a reduced scattering coefficient of 5  cm1. Nigrosin dye (Sigma-Aldrich 198285) was added to provide an absorption coefficient of 0.05  cm1 (at 564 nm, peak of the nigrosin spectrum). The solution was poured into a 20-mL (2-cm diameter) syringe with the injection end removed and with polyvinyl chloride tubing (inner diameter: 1.5 mm, outer diameter: 2.1 mm; VWR 228-3857) inserted along the central axis. After the agar was set, the phantom was removed from the syringe ready for imaging.

PAI experiments were performed using a commercial small animal imaging system (MSOT inVision 256-TF; iThera Medical GmbH). The system has been described in detail elsewhere.37,38 Briefly, a tunable (660 to 1300 nm) optical parametric oscillator (OPO), pumped by a nanosecond (ns) pulsed Nd:YAG laser, with 10-Hz repetition rate and up to 7-ns pulse duration provides excitation pulses. Light is delivered to the sample through a custom optical fiber assembly, which creates a diffuse ring of uniform illumination over the imaging plane. The sample is coupled to the transducers using a water bath, filled with degassed, and deionized water. For ultrasound detection, 256 toroidally focused ultrasound transducers covering an angle of 270 deg are used (center frequency of 5 MHz, 60% bandwidth) allowing tomographic reconstruction. A minimum of four images were taken along the length of the phantom at steps of 0.5 mm using seven wavelengths (700, 730, 760, 800, 850, 900, and 1040 nm) with an average of 10 pulses per wavelength.

For the measurements of the ODC, a flow phantom set up was used, which has also been described in detail elsewhere.39 For each measurement, 5 mL of pooled blood from the respective species was first oxygenated by the addition of 0.2% v/v hydrogen peroxide [H2O2 30% (w/w) in deionized water, Sigma-Aldrich 7722-84-1]. The oxygenated blood was filled into the circuit, carefully avoiding the introduction of air bubbles. During the course of the experiment, a syringe driver (Harvard, MKCB2159V) was used to deoxygenate the blood with 0.03% w/v sodium hydrosulfite (ACROS Organics 7775-14-6) in PBS) at a constant flow rate of 10  μL/min. The experiment was performed at room temperature and a peristaltic pump (Fisher Scientific CTP100) was used for blood circulation. Oxygen fluorescence quenching needle probes (Oxford Optronix, NX-BF/O/E) were placed before and after the tissue-mimicking phantom, which recorded the temperature and partial pressure of oxygen (pO2, mmHg) in real time. The data were downloaded via an Arduino UNO and read in MATLAB®. Using the same commercial PAI system as above, images were acquired at a single position (no pulse-to-pulse averaging) for 17 wavelengths (660, 664, 680, 684, 694, 700, 708, 715, 730, 735, 760, 770, 775, 779, 800, 850, and 950 nm). Absorption spectra were measured using a light source (Avantes Avalight-HAL-S-Mini) and spectrometer (AvaSpec-ULS2048-USB2-VA-50). Absorption spectra were recorded continuously via AvaSoft software as the fluid passed through a flow cell (Hellma Analytics, 170700-0.5-40) as it has been shown that directly measured absorption spectra provide the most accurate endmembers for spectral unmixing.39 Species-specific absorption spectra at the point of complete oxygenation and deoxygenation were extracted and used for spectral unmixing of the data recorded for the respective species. Between each measurement run, the tubing containing the blood was cleaned with PBS.

For the experiments involving naked mole-rat blood, a simpler set up was used as only a limited amount of naked mole-rat blood could be obtained. Blood samples (100  μL) were inserted into a straw within a phantom and 10  μL H2O2 (0.03% in PBS) was injected using a syringe. The oxygenation of the blood was directly measured after injection using MSOT. Thirty images were taken of a chosen slice using 7 wavelengths (700, 730, 760, 800, 850, 900, and 1040 nm), with an average of 10 pulses per wavelength. Each image took 7 s to acquire.

Measurements were conducted at 37°C. It should be noted here that naked-mole rats are considered poikilothermic and usually have a physiological body temperature of around 30°C to 32°C.40 Examining the oxygen affinity at higher temperature decreases the oxygen affinity of the blood.41 However, it has been shown that at 37°C significant differences between the oxygen affinity of naked mole-rat and mouse are still present,42 supporting our experimental approach.

2.6.

Image and Statistical Analysis

PAI data were analyzed using the ViewMSOT software (v3.6.0.119; iThera Medical GmbH). Model-based image reconstruction and linear multispectral processing were applied to extract the relative signal contributions of HbO2 and HbR. The same position within the phantom was used to determine the average intensities for HbO2 and HbR. Regions of interest (ROIs) were manually drawn around the circular cross section of the tube inserted in the phantom. THbMSOT was calculated as the sum of HbO2 and HbR. sO2MSOT was calculated as the ratio of HbO2 to THbMSOT signal. The expressions THbMSOT and sO2MSOT are used to emphasize that the photoacoustically determined THb and sO2 biomarkers are not exactly equal to the underlying parameters, since to be able to accurately resolve absolute values, knowledge of the light fluence distribution, system response, and Grüneisen parameter is required.43 For dynamic experiments, a trendline was fitted on the increasing sO2 values and the maximum value reached during the experiment and gradient of the trendline were extracted, to be compared between the species.

Raw data extracted from the ROIs were analyzed using Python (version 2.7) and MATLAB. Statistical analysis was performed using Prism (GraphPad). All data are shown as mean±standard error of the mean (SEM) unless otherwise stated. Unpaired two-tailed t-tests were performed to calculate the statistics. Pearson’s rank test was performed to assess correlations between biochemical blood parameters and THbMSOT. Significance is assigned for p-values <0.05.

3.

Results

3.1.

Gene sequence Analysis Confirms Intra- and Interspecies Homogeneity of Hemoglobin Genes in Mouse and Rat

We first assessed the intraspecies genetic correspondence of the two α- and two β-globin chains of the HB tetramer in rat and mouse, as alterations in HB globin genes can lead to changes in the protein structure that might affect the PAI signal. We found that an intraspecies homogeneity of 100% could be determined (n=3, Table 1). These results indicate that intraspecies signal variations in the following experiment are unlikely to be caused by genetic missense mutations, but rather by other physiological or technical sources.

3.2.

PAI THb Correlates Directly with Biochemical HB Values Obtained Using a Hematology Analyzer and Detects Intraspecies Variations

The relationship between the THbMSOT biomarker and biochemical blood parameters was examined. THbMSOT values from photoacoustic images taken from our static blood phantoms correlated with biochemical HB [Fig. 1(a)], HCT [Fig. 1(b)], and RBC [Fig. 1(c)] in the same mouse blood sample (HB: Pearson r=0.6867, p=0.0047; HCT: Pearson r=0.6083, p=0.0161; RBC: Pearson r=0.5594, p=0.0302). To determine whether these observations are species-independent, the correlation between THbMSOT and biochemical blood parameters was also studied in rat. Again, a significant correlation was found between THbMSOT and biochemical HB [Fig. 1(d)], HCT [Fig. 1(e)], and RBC [Fig. 1(f)] in rat blood (HB: Pearson r=0.6786, p=0.0076; HCT: Pearson r=0.5363, p=0.0480; and RBC: Pearson r=0.5474, p=0.0427) suggesting that PAI is directly sensitive to intraspecies variations in HB, HCT, and RBC parameters.

Fig. 1

THbMSOT biomarker measured in tissue-mimicking phantoms corresponds to biochemical HB, HCT, and RBC levels in mouse and rat. THbMSOT extracted from PAI data obtained from tissue-mimicking phantoms containing blood from female mice [n=15, purple circles, (a)–(c)] and rats [n=14, pink squares, (d)–(f)] correlated significantly with biochemical (a), (d) HB; (b), (e) HCT; and (c), (f) RBC count concentration in both species. * p<0.05, ** p<0.01 by Pearson correlation.

JBO_25_9_095002_f001.png

3.3.

PAI THb Resolves Interspecies Differences in HB and HCT in vitro

After assessing the impact of intraspecies variation in biochemical blood parameters on MSOT signal, the effect of interspecies differences was analyzed. In order to exclude major differences in the protein structure and conformation yielding differences in the absorption spectra for the HB, we first confirmed that no interspecies difference in the absorption spectra of HB extracted from mouse and rat blood was detected. Minor differences in total light absorbance of the samples were observed, which could be explained by different HB levels of rat and mouse blood (Table 2); thus, absorption spectra were normalized to the area under the curve for further comparison. In line with the literature,44,45 the experimentally determined spectra of mouse and rat blood demonstrated a very good agreement [Fig. 2(a)].

Table 2

Comparison of experimental and literature HB and HCT values of mouse (n=15) and rat (n=14).

ParameterMouse (BALB/c)Rat (Wistar)
HB (exp.) (g/dL) (mean±std)14.73±2.0915.06±2.35
HB (lit.) (g/dL)11 to 1614.4 to 18.0
HCT (exp.) (%) (mean±std)41.83±6.3845.50±6.13
HCT (lit.) (%)37 to 5236 to 48
ReferenceSantos et al. (2016)34Charles River Laboratories, 199835

Fig. 2

PAI resolves interspecies HB differences in rat and mouse. (a) Absorption spectra of HB extracted from lysed whole blood of mouse (purple) and rat (pink). (b) Interspecies differences in THbMSOT obtained from PAI measurements of female mouse (n=15) and rat (n=14) blood show a significantly higher value in rat blood, which was underscored by differences in biochemical HB (c) and HCT (d). Notably, the RBC value (e) was found to be lower in the rat, but with higher mean corpuscular hemoglobin (MCH) per red blood cell (f). While the mean corpuscular hemoglobin concentration (MCHC) was comparable between the species (g), the mean corpuscular volume MCV (h) was higher in the rat. Data are represented as mean±SEM, significance *** p<0.0001 by unpaired t-test.

JBO_25_9_095002_f002.png

Next, THbMSOT was compared to the biochemical blood parameters of the different species. A significantly higher THbMSOT level was observed for the rat [p<0.0001, Fig. 2(b)]. Corresponding to this observation, significantly higher HB [Fig. 2(c)] and HCT [Fig. 2(d)] values could be observed in this species (p<0.0001). Interestingly, the rat was characterized by significantly lower RBC levels [Fig. 2(e)]. We compared the MCH levels between the two species to establish whether this observation was associated with differences in the average HB amount per RBC. In correspondence to the HB and HCT values, significant higher MCH levels were found in the rat [Fig. 2(f), p<0.0001]. A more detailed analysis revealed that the higher MCH values in the rat do not originate from a higher MCHC value [Fig. 2(g)], but rather from a larger MCV of the RBC [p<0.0001, Fig. 2(h)]. These results suggest that PAI correctly resolves interspecies differences in HB and HCT in vitro, but can only be used to quantitatively compare interspecies RBC values when MCH values are within the same range.

3.4.

PAI Resolves Interspecies Differences in Oxygenation Dynamics

We next examined whether PAI has sufficient sensitivity to capture interspecies differences in oxygenation dynamics in mice, rat, and human blood based on the known differences in ODCs in the literature between these species [Fig. 3(a)].26 Under standard conditions [pH=7.4, pCO2=40  mmHg (5.3 kPa), temperature = 37°C, carboxyhemoglobin <2%], human HB is known to have the highest oxygen affinity with an ODC shifted farthest to the left (p50std=26  mmHg), followed by rat (p50std=32  mmHg), and then mouse HB (p50std=48.5  mmHg)26 [Fig. 3(a)].

Fig. 3

PAI resolves interspecies differences in ODCs between mouse, rat, and human. Oxygenated mouse (n=2), rat (n=4), and human (n=5) blood samples were deoxygenated in a dynamic flow phantom circuit at room temperature and changes in absorption spectrum, pO2, and PAI signals were recorded. (a) Literature values for ODCs of the respective species under standard conditions.26 (b) sO2MSOT was calculated from PAI images using the absorption spectra measured within the flow phantom circuit for spectral unmixing. The resulting values were plotted against the measured pO2 of the blood within the circuit at the same time point in order to produce an ODC. (c) Extracted p50MSOT values denoting the pO2 at 50% sO2MSOT are shown. It should be noted that the ODC influencing factors 2,3-DPG concentration, acid–base balance, and amount of dyshemoglobins were not standardized, but rather reflect the physiological values found in the respective species.

JBO_25_9_095002_f003.png

To test whether MSOT is able to qualitatively resolve these differences, fully oxygenated blood was introduced into a dynamic flow phantom system and the blood was gradually deoxygenated while the absorption spectra, pO2, temperature, and PAI spectral data were recorded in real time. The species-specific ODCs were compared by plotting sO2MSOT against pO2 and showed broadly similar results as reported in the literature25,26 [Fig. 3(b)]. In a similar pattern, the human ODC was found to be shifted farthest to the left (p50MSOT=29.00±2.94  mmHg), followed by the rat (p50MSOT=77.75±4.99  mmHg), and then the mouse (p50MSOT=93.50±3.54  mmHg) [Fig. 3(c)]. These results indicate that MSOT is capable of qualitatively resolving interspecies oxygenation dynamics in vitro.

3.5.

PAI is Sensitive to the Enhanced Oxygen Dissociation Curve of the Naked Mole-Rat

Having established the capacity for PAI to resolve differences in both HB parameters and oxygenation dynamics in mice and rats, we made a first PAI study of blood from the naked mole-rat. The naked mole-rat is adapted to live in a hypoxic underground environment, which involves having a higher affinity for oxygen in its HB molecules than rodents of similar size.42

As only small amounts of naked mole-rat blood could be obtained, a simpler experimental design was used for the studies in which small blood samples were oxygenated with H2O2 (0.03% in PBS) and the oxygenation measured using PAI during 4 min. A significantly higher maximum sO2MSOT after 4 min could be found for the naked mole-rat blood in comparison to the same experiment conducted with mouse and rat blood [Fig. 4(a)]. Some deviation is observed between the maximum sO2MSOT obtained for the rat in this experiment compared to the earlier findings (Fig. 3), although it is still within the bounds of error. The deviation is likely due to experiments being conducted on different batches of blood provided by an external supplier, which may have experienced different extractions or handling beyond our control. Considering the sigmoidal shape of the ODC, the highest maximum sO2MSOT observed in the naked mole-rat should be accompanied with the lowest gradient of the oxygenation change, which was indeed found in our experiments [Fig. 4(b)]. The results suggest that PAI is able to resolve the higher oxygen affinity of the naked-mole rat blood42 compared to mouse and rat, indicated by the overall higher sO2 and lower change of sO2 when oxygenating the samples under similar experimental conditions.

Fig. 4

PAI evaluation of oxygenation dynamics is sensitive to the higher oxygen affinity of naked mole-rat blood. Blood samples taken from mouse (n=4), rat (n=4), and naked mole-rat (NMR; n=5) were oxygenated using 10  μL hydrogen peroxide (0.03% in PBS) at 37°C. (a) The maximum sO2MSOT was highest in the naked mole-rat. (b) Considering the sigmoidal shape of the ODC, this would be expected to lead to the lowest gradient of the oxygenation change, which was confirmed in our experiments.

JBO_25_9_095002_f004.png

4.

Discussion

PAI holds substantial potential for application in the measurement of THb and sO2 biomarkers, however, our understanding of the relationship between these biomarkers and the biochemical parameters of blood including HB concentration, HCT, and ODCs has yet to be fully elucidated. Here, we sought to study the sensitivity of PAI toward physiological variations of HB, including both intra- and interspecies variations.

Our results indicate that PAI determined THbMSOT and sO2MSOT can resolve physiological variations in HB and HCT both within and between species. We found significant linear correlations of THbMSOT with HB, HCT, and RBC count. As HCT and HB linearly depend on each other (roughly HB=HCT/3),46 it is unsurprising that both show linear trends. Our findings indicate that when the concentration of corpuscular HB is comparable between subjects, differences in RBC count could, in principle, be determined using PAI, however, this would require a low variance of the corpuscular HB value within the investigated group of subjects. Even within species, significant variations in MCH can occur due to disease-related macro- or microcytic anemia or age,47 which should be considered when making any conclusions from PAI signal to RBC number.

We could further show that differences in interspecies oxygenation dynamics based on the measurement of the PAI biomarker sO2MSOT could be clearly resolved. This is notable given the diversity of species studied. In particular, our studies revealed that sO2MSOT is sensitive to the differences in ODCs between mouse, rat, and human, as well as naked mole-rat. It has to be noted that our study only aimed for a qualitative comparison of the ODC curves between the species. For a quantitative comparison of ODC characteristics and p50 values, other ODC influencing factors, such as species-specific 2,3-diphosphoglycerate (2,3-DPG) concentration,48 pH,49 amount of dyshemoglobins,41 or the partial pressure of carbon dioxide (pCO2)50 would also need to be taken into consideration.

Our study indicates that the application of PAI to detect functional differences in HB should be considered carefully when comparing data obtained from different species, particularly when using an “oxygen-enhanced” or “gas challenge” imaging protocol.10,51 It also highlights the exciting potential of PAI derived biomarkers to be applied for studies in disease-associated anaemia52 or hemoglobinopathies, such as sickle cell anemia.53 Patients with hemoglobinopathies where globin proteins are structurally abnormal often show shifts in the ODC,54 which may be captured using PAI.

Despite these promising findings, there remain some limitations of the study. We used a standard linear spectral unmixing method for resolving the contributions of HbR and HbO2 to our signals, which produced values of sO2MSOT with limited dynamic range, particularly at the higher end of the ODCs, when compared to ground truth. Employing more advanced multispectral processing techniques should further enhance the accuracy of the photoacoustically determined sO2 estimation, however, improved spectral classification methods remain a topic of active research in the field and an optimal solution has yet to be reached.5557

Further, our in vitro phantom experiments provide a well-controlled reference measurement for both the HB absorption spectrum and the partial pressure of oxygen. Although it may be possible in future work to use the knowledge of interspecies variations observed in this study to make a qualitative in vivo comparison of the PAI biomarker response, for further validation of our findings, in vivo confirmation would be advantageous. Unfortunately, the experimental design would be complex because of the imaging artifacts that arise due to: tissue heterogeneities, which lead to local variations in optical and acoustic properties, and consequently to uncertainty in PAI fluence distributions58,59 motion, such as breathing or heartbeart;60 and anatomical positioning within a nonrigid animal holder.36 Furthermore, factors influencing blood extraction, such as blood clotting, hemolysis, dilution of blood with interstitial fluid, or varying lag time before the analysis of the sample can affect the determination of the biochemical blood parameters.61 Nonetheless, such studies are of particular importance when PAI is performed in a clinical environment, as globin gene mutations in humans are common, affecting around 7% of the overall population.62,63 Moreover, HB concentrations have been found to vary with human ethnic group,6466 which could impact the acquired results in studies of mixed populations.

In summary, our findings highlight the encouraging capacity of PAI to resolve intra- and interspecies differences in HB-related blood parameters and oxygenation dynamics in vitro in a sensitive and label-free manner. These results suggest promising future avenues for application of PAI for HB- and oxygenation-related research studies, strengthening the position of PAI as a powerful and versatile tool in biomedicine.

Disclosures

J. B., I. Q G., and S. E. B. have previously received research support from iThera Medical GmbH and Cyberdyne Inc.

Acknowledgments

The authors thank the Biorepository Unit (BRU, Matthew Clayton) of the CRUK Cambridge Institute for their assistance with mouse and rat blood and Dr. Doris Rassl and the Royal Papworth Hospital tissue bank for providing the human blood. This work was funded by Cancer Research UK under Grant Nos. C14303/A17197, C9545/A29580, C47594/A16267, and C197/A16465 (SEB) as well as C56829/A22053 (EStJS). L. H. is funded by a studentship from the National Physical Laboratory.

Code, Data, and Materials Availability

All associated code and raw data for this manuscript are available in the University of Cambridge repository at: https://doi.org/10.17863/CAM.55512. All materials used in the preparation of phantoms are readily available from commercial suppliers as detailed herein and further in Ref. 39.

References

1. 

A. G. Bell, “On the production and reproduction of sound by light,” Am. J. Sci., s3-20 305 –324 (1880). https://doi.org/10.2475/ajs.s3-20.118.305 AJSCAP 0002-9599 Google Scholar

2. 

A. Taruttis and V. Ntziachristos, “Advances in real-time multispectral optoacoustic imaging and its applications,” Nat. Photonics, 9 219 –227 (2015). https://doi.org/10.1038/nphoton.2015.29 NPAHBY 1749-4885 Google Scholar

3. 

P. Beard, “Biomedical photoacoustic imaging,” Interface Focus, 1 602 –631 (2011). https://doi.org/10.1098/rsfs.2011.0028 Google Scholar

4. 

G. Diot et al., “Multispectral optoacoustic tomography (MSOT) of human breast cancer,” Clin. Cancer Res., 23 6912 –6922 (2017). https://doi.org/10.1158/1078-0432.CCR-16-3200 Google Scholar

5. 

I. Quiros-Gonzalez et al., “Optoacoustics delineates murine breast cancer models displaying angiogenesis and vascular mimicry,” Br. J. Cancer, 118 1098 –1106 (2018). https://doi.org/10.1038/s41416-018-0033-x BJCAAI 0007-0920 Google Scholar

6. 

M. Heijblom et al., “Photoacoustic image patterns of breast carcinoma and comparisons with magnetic resonance imaging and vascular stained histopathology,” Sci. Rep., 5 11778 (2015). https://doi.org/10.1038/srep11778 SRCEC3 2045-2322 Google Scholar

7. 

I. Stoffels et al., “Metastatic status of sentinel lymph nodes in melanoma determined noninvasively with multispectral optoacoustic imaging,” Sci. Transl. Med., 7 317ra199 (2015). https://doi.org/10.1126/scitranslmed.aad1278 STMCBQ 1946-6234 Google Scholar

8. 

G. C. Langhout et al., “Detection of melanoma metastases in resected human lymph nodes by noninvasive multispectral photoacoustic imaging,” Int. J. Biomed. Imaging, 2014 1 –7 (2014). https://doi.org/10.1155/2014/163652 Google Scholar

9. 

V. S. Dogra et al., “Multispectral photoacoustic imaging of prostate cancer: preliminary ex-vivo results,” J. Clin. Imaging Sci., 3 41 (2013). https://doi.org/10.4103/2156-7514.119139 Google Scholar

10. 

M. R. Tomaszewski et al., “Oxygen-enhanced and dynamic contrast-enhanced optoacoustic tomography provide surrogate biomarkers of tumor vascular function, hypoxia, and necrosis,” Cancer Res., 78 5980 –5991 (2018). https://doi.org/10.1158/0008-5472.CAN-18-1033 CNREA8 0008-5472 Google Scholar

11. 

X. Yang and L. Xiang, “Photoacoustic imaging of prostate cancer,” J. Innov. Opt. Health Sci., 10 1730008 (2017). https://doi.org/10.1142/S1793545817300087 Google Scholar

12. 

W. Roll et al., “Multispectral optoacoustic tomography of benign and malignant thyroid disorders—a pilot study,” J. Nucl. Med., 60 (10), 1461 –1466 (2019). https://doi.org/10.2967/jnumed.118.222174 JNMEAQ 0161-5505 Google Scholar

13. 

A. Rosenthal, F. A. Jaffer and V. Ntziachristos, “Intravascular multispectral optoacoustic tomography of atherosclerosis: prospects and challenges,” Imaging Med., 4 299 –310 (2012). https://doi.org/10.2217/iim.12.20 Google Scholar

14. 

A. Taruttis et al., “Optoacoustic imaging of human vasculature: feasibility by using a handheld probe,” Radiology, 281 256 –263 (2016). https://doi.org/10.1148/radiol.2016152160 RADLAX 0033-8419 Google Scholar

15. 

I. Ivankovic et al., “Real-time volumetric assessment of the human carotid artery: handheld multispectral optoacoustic tomography,” Radiology, 291 45 –50 (2019). https://doi.org/10.1148/radiol.2019181325 RADLAX 0033-8419 Google Scholar

16. 

N. Bhutiani et al., “Noninvasive imaging of colitis using multispectral optoacoustic tomography,” J. Nucl. Med., 58 1009 –1012 (2017). https://doi.org/10.2967/jnumed.116.184705 JNMEAQ 0161-5505 Google Scholar

17. 

A. Brown and A. L. Goodall, “Normal variations in blood haemoglobin concentration,” J. Physiol., 104 (4), 404 –407 (1946). https://doi.org/10.1113/jphysiol.1946.sp004133 JPHYA7 0022-3751 Google Scholar

18. 

J. W. Adamson and C. A. Finch, “Hemoglobin function, oxygen affinity, and erythropoietin,” Annu. Rev. Physiol., 37 351 –369 (1975). https://doi.org/10.1146/annurev.ph.37.030175.002031 ARPHAD 0066-4278 Google Scholar

19. 

M. A. Johnson-Spear and R. Yip, “Hemoglobin difference between black and white women with comparable iron status: justification for race-specific anemia criteria,” Am. J. Clin. Nutr., 60 117 –121 (1994). https://doi.org/10.1093/ajcn/60.1.117 Google Scholar

20. 

X. Dong et al., “A population-based study of hemoglobin, race, and mortality in elderly persons,” J. Gerontol. Ser. A, 63 873 –878 (2008). https://doi.org/10.1093/gerona/63.8.873 Google Scholar

21. 

W. W. Hawkins, E. Speck and V. G. Leonard, “Variation of the hemoglobin level with age and sex,” Blood, 9 999 –1007 (1954). https://doi.org/10.1182/blood.V9.10.999.999 BLOOAW 0006-4971 Google Scholar

22. 

C. S. Williamson, “Influence of age and sex on hemoglobin,” Arch. Intern. Med., XVIII 505 (1916). https://doi.org/10.1001/archinte.1916.00080170078006 AIMDAP 0003-9926 Google Scholar

23. 

B. Vahlquist, “The cause of the sexual differences in erythrocyte hemoglobin and serum iron levels in human adults,” Blood, 5 874 –875 (1950). https://doi.org/10.1182/blood.V5.9.874.874 BLOOAW 0006-4971 Google Scholar

24. 

D. M. Mintzer, S. N. Billet and L. Chmielewski, “Drug-induced hematologic syndromes,” Adv. Hematol., 2009 1 –11 (2009). https://doi.org/10.1155/2009/495863 Google Scholar

25. 

L. H. Gray and J. M. Steadman, “Determination of the oxyhaemoglobin dissociation curves for mouse and rat blood,” J. Physiol., 175 161 –171 (1964). https://doi.org/10.1113/jphysiol.1964.sp007509 JPHYA7 0022-3751 Google Scholar

26. 

K. Schmidt-Neilsen and J. L. Larimer, “Oxygen dissociation curves of mammalian blood in relation to body size,” Am. J. Physiol., 195 (2), 424 –428 (1958). https://doi.org/10.1152/ajplegacy.1958.195.2.424 Google Scholar

27. 

B. G. Forget and H. F. Bunn, “Classification of the disorders of hemoglobin,” Cold Spring Harbor perspectives in medicine, 3 (2), a011684 (2013). https://doi.org/10.1101/cshperspect.a011684 Google Scholar

28. 

T. J. Park et al., “Fructose-driven glycolysis supports anoxia resistance in the naked mole-rat,” Science, 356 307 –311 (2017). https://doi.org/10.1126/science.aab3896 SCIEAS 0036-8075 Google Scholar

29. 

A. Untergasser et al., “Primer3—new capabilities and interfaces,” Nucleic Acids Res., 40 e115 –e115 (2012). https://doi.org/10.1093/nar/gks596 NARHAD 0305-1048 Google Scholar

31. 

F. Sievers, D. G. Higgins, “Clustal omega, accurate alignment of very large numbers of sequences,” Methods in Molecular Biology, 1079 105 –116 Humana Press, Totowa, New Jersey (2014). Google Scholar

32. 

A. K. Wassmuth et al., “Evaluation of the Mythic 18 hematology analyzer for use with canine, feline, and equine samples,” J. Vet. Diagn. Invest., 23 436 –453 (2011). https://doi.org/10.1177/1040638711403416 Google Scholar

33. 

S. F. Russo and R. B. Sorstokke, “Hemoglobin. Isolation and chemical properties,” J. Chem. Educ., 50 347 –350 (1973). https://doi.org/10.1021/ed050p347 JCEDA8 0021-9584 Google Scholar

34. 

E. W. Santos et al., “Valores de referência hematológicos e bioquímicos para camundongos das linhagens C57BL/6, Swiss Webster e BALB/c,” Braz. J. Veterinary Res. Anim. Sci., 53 (2), 138 –145 (2016). https://doi.org/10.11606/issn.1678-4456.v53i2p138-145 Google Scholar

35. 

Charles River Laboratories, “Baseline hematology and clinical chemistry values for Charles River Wistar Rat.,” (1998). https://www.criver.com/sites/default/files/resources/BaselineHematologyandClinicalChemistryValuesforCharlesRiverWistarRats%5BCrlWIBR%5DasaFunctionofSexandAgeSpring1998.pdf Google Scholar

36. 

J. Joseph et al., “Evaluation of precision in optoacoustic tomography for preclinical imaging in living subjects,” J. Nucl. Med., 58 807 –814 (2017). https://doi.org/10.2967/jnumed.116.182311 JNMEAQ 0161-5505 Google Scholar

37. 

S. Morscher et al., “Semi-quantitative multispectral optoacoustic tomography (MSOT) for volumetric PK imaging of gastric emptying,” Photoacoustics, 2 103 –110 (2014). https://doi.org/10.1016/j.pacs.2014.06.001 Google Scholar

38. 

A. Dima, N. C. Burton and V. Ntziachristos, “Multispectral optoacoustic tomography at 64, 128, and 256 channels,” J. Biomed. Opt., 19 036021 (2014). https://doi.org/10.1117/1.JBO.19.3.036021 JBOPFO 1083-3668 Google Scholar

39. 

M. Gehrung, S. E. Bohndiek and J. Brunker, “Development of a blood oxygenation phantom for photoacoustic tomography combined with online pO2 detection and flow spectrometry,” J. Biomed. Opt., 24 121908 (2019). https://doi.org/10.1117/1.JBO.24.12.121908 JBOPFO 1083-3668 Google Scholar

40. 

B. K. McNab, “The metabolism of fossorial rodents: a study of convergence,” Ecology, 47 712 –733 (1966). https://doi.org/10.2307/1934259 ECGYAQ 0094-6621 Google Scholar

41. 

S. Patel and S. S. Mohiuddin, Physiology, Oxygen Transport and Carbon Dioxide Dissociation Curve, StatPearls Publishing, Treasure Island, Florida (2020). Google Scholar

42. 

K. Johansen et al., “Blood respiratory properties in the naked mole rat Heterocephalus glaber, a mammal of low body temperature,” Respir. Physiol., 28 303 –314 (1976). https://doi.org/10.1016/0034-5687(76)90025-6 RSPYAK 0034-5687 Google Scholar

43. 

B. Cox et al., “Quantitative spectroscopic photoacoustic imaging: a review,” J. Biomed. Opt., 17 061202 (2012). https://doi.org/10.1117/1.JBO.17.6.061202 JBOPFO 1083-3668 Google Scholar

44. 

D. A. Grosenbaugh, J. O. Alben and W. W. Muir, “Absorbance spectra of inter-species hemoglobins in the visible and near infrared regions,” J. Vet. Emergency Crit. Care, 7 36 –42 (1997). https://doi.org/10.1111/j.1476-4431.1997.tb00042.x Google Scholar

45. 

W. G. Zijlstra et al., “Spectrophotometry of hemoglobin: absorption spectra of rat oxyhemoglobin, deoxyhemoglobin, carboxyhemoglobin, and methemoglobin,” Comp. Biochem. Physiol. Part B, 107 161 –166 (1994). https://doi.org/10.1016/0305-0491(94)90238-0 CBPBB8 0305-0491 Google Scholar

46. 

L. Quintó et al., “Relationship between haemoglobin and haematocrit in the definition of anaemia,” Trop. Med. Int. Health, 11 1295 –1302 (2006). https://doi.org/10.1111/j.1365-3156.2006.01679.x Google Scholar

47. 

T. I. Restell et al., “Hematology of Swiss mice (Mus musculus) of both genders and different ages,” Acta Cirurgica Bras., 29 306 –312 (2014). https://doi.org/10.1590/S0102-86502014000500004 Google Scholar

48. 

R. Macdonald, “Red cell 2,3-diphosphoglycerate and oxygen affinity,” Anaesthesia, 32 544 –553 (1977). https://doi.org/10.1111/j.1365-2044.1977.tb10002.x Google Scholar

49. 

G. S. Adair, “The hemoglobin system. VI. The oxygen dissociation curve of hemoglobin,” J. Biol. Chem., 63 529 –545 (1925). Google Scholar

50. 

R. M. Winslow et al., “Simulation of continuous blood O2 equilibrium curve over physiological pH, DPG, and PCO2 range,” J. Appl. Physiol., 54 524 –529 (1983). https://doi.org/10.1152/jappl.1983.54.2.524 JARPDU 0161-7567 Google Scholar

51. 

M. R. Tomaszewski et al., “Oxygen enhanced optoacoustic tomography (OE-OT) reveals vascular dynamics in murine models of prostate cancer,” Theranostics, 7 2900 –2913 (2017). https://doi.org/10.7150/thno.19841 Google Scholar

52. 

W.-P. Pan, Y.-H. Teng and J. Shen, “Photoacoustic study of iron-deficiency anaemia,” J. Biomed. Eng., 13 415 –416 (1991). https://doi.org/10.1016/0141-5425(91)90023-Z JBIEDR 0141-5425 Google Scholar

53. 

C. Cai et al., “Photoacoustic flow cytometry for single sickle cell detection in vitro and in vivo,” Anal. Cell. Pathol., 2016 1 –11 (2016). https://doi.org/10.1155/2016/2642361 ACPAER 0921-8912 Google Scholar

54. 

J.-A. Collins et al., “Relating oxygen partial pressure, saturation and content: the haemoglobin–oxygen dissociation curve,” Breathe, 11 194 –201 (2015). https://doi.org/10.1183/20734735.001415 Google Scholar

55. 

S. Tzoumas et al., “Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues,” Nat. Commun., 7 12121 (2016). https://doi.org/10.1038/ncomms12121 NCAOBW 2041-1723 Google Scholar

56. 

J. Gröhl et al., “Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI),” Med. Phys., (2019). https://arxiv.org/abs/1902.05839 Google Scholar

57. 

S. Tzoumas and V. Ntziachristos, “Spectral unmixing techniques for optoacoustic imaging of tissue pathophysiology,” Philos. Trans. R. Soc. A, 375 20170262 (2017). https://doi.org/10.1098/rsta.2017.0262 PTRMAD 1364-503X Google Scholar

58. 

B. Liu et al., “Phantom and in-vivo measurements of hemoglobin concentration and oxygen saturation using PCT-S small animal scanner,” Proc. SPIE, 6437 64371X (2007). https://doi.org/10.1117/12.701376 PSISDG 0277-786X Google Scholar

59. 

B. T. Cox, J. G. Laufer and P. C. Beard, “The challenges for quantitative photoacoustic imaging,” Proc. SPIE, 7177 717713 (2009). https://doi.org/10.1117/12.806788 PSISDG 0277-786X Google Scholar

60. 

X. L. Deán-Ben and D. Razansky, “Adding fifth dimension to optoacoustic imaging: volumetric time-resolved spectrally enriched tomography,” Light Sci. Appl., 3 e137 –e137 (2014). https://doi.org/10.1038/lsa.2014.18 Google Scholar

61. 

L. Berkow, “Factors affecting hemoglobin measurement,” J. Clin. Monit. Comput., 27 499 –508 (2013). https://doi.org/10.1007/s10877-013-9456-3 Google Scholar

62. 

E. Kohne, “Hemoglobinopathies,” Dtsch. Arztebl. Int., 108 532 –540 (2011). https://doi.org/10.3238/arztebl.2011.0532 Google Scholar

63. 

D. J. Weatherall and J. B. Clegg, “Inherited haemoglobin disorders: an increasing global health problem,” Bull. World Health Organ., 79 704 –12 (2001). BWHOA6 Google Scholar

64. 

J. L. Cresanta et al., “Racial difference in hemoglobin concentration of young adults,” Prev. Med., 16 659 –669 (1987). https://doi.org/10.1016/0091-7435(87)90049-1 PVTMA3 0091-7435 Google Scholar

65. 

D. M. Williams, “Racial differences of hemoglobin concentration: measurements of iron, copper, and zinc,” Am. J. Clin. Nutr., 34 1694 –1700 (1981). https://doi.org/10.1093/ajcn/34.9.1694 Google Scholar

66. 

X. Dong et al., “A population-based study of hemoglobin, race, and mortality in elderly persons,” J. Gerontol. A, 63 873 –878 (2008). https://doi.org/10.1093/gerona/63.8.873 Google Scholar

Biography

Lina Hacker received her bachelor’s degree in molecular biomedicine at the University of Bonn in Germany and her master’s degree in biomedical engineering at RWTH Aachen in Germany. Currently, she is pursuing a PhD in Medical Sciences at the University of Cambridge focussing on the technical validation of optoacoustic imaging systems.

Biographies of the other authors are not available.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Lina Hacker, Joanna Brunker, Ewan S. Smith, Isabel Quirós-Gonzalez, and Sarah E. Bohndiek "Photoacoustics resolves species-specific differences in hemoglobin concentration and oxygenation," Journal of Biomedical Optics 25(9), 095002 (4 September 2020). https://doi.org/10.1117/1.JBO.25.9.095002
Received: 3 March 2020; Accepted: 11 August 2020; Published: 4 September 2020
Lens.org Logo
CITATIONS
Cited by 14 scholarly publications.
Advertisement
Advertisement
KEYWORDS
Blood

Absorption

Oxygen

Photoacoustic spectroscopy

In vitro testing

Tissues

Statistical analysis

Back to Top