Method for determining whether the number of hepatocytes in rat liver is increased after treatment with the peroxisome proliferator gemfibrozil

The use of design-based stereology to evaluate volumes and numbers in the liver: a review with practical guidelines

Stereology offers a number of tools for the analysis of sections in microscopy (which usually provide only two-dimensional information) for the purpose of estimating geometric quantities, such as volume, surface area, length or number of particles (cells or other structures). The use of these tools enables recovery of the three-dimensional information that is inherent in biological tissues. This review uses the liver as a paradigm for summarizing the most commonly used state-of-the-art methods for quantitation in design-based stereology. Because it is often relevant to distinguish hyperplasia and hypertrophy in liver responses, we also focus on potential pitfalls in the sampling and processing of liver specimens for stereological purposes, and assess the existing methods for volume and number estimation. With respect to volume, we considered whole liver volume (V), volume density (V(V)) and so-called local volumes, including the number-weighted volume (V(N)) and the volume-weighted volume (V(V)). For number, we considered the total number (N) and the numerical density (N(V)). If correctly applied, current stereological methods guarantee that no bias is introduced in the estimates, which will be therefore accurate; additionally, methods can be tuned for obtaining precise quantitative estimates that can reveal subtle changes in the volume or number of selected hepatic cells. These methods have already detailed the effects of some substances and specific diets on the liver, and should be routinely included in the toolbox of liver research.

Noninvasive quantification of tumor volume in preclinical liver metastasis models using contrast-enhanced x-ray computed tomography

OBJECTIVES

To determine a timepoint after contrast injection that yields equal liver parenchymal and vascular enhancement in micro-computed tomography images. To evaluate the utility of images acquired during this time period for the noninvasive measurement of liver-tumor volume.

MATERIALS AND METHODS

The imaging timepoint was determined by quantifying the enhancement kinetics of Fenestra VC (0.015 mL/g) in NIH III mice. In respiratory-gated images of tumor bearing mice, the ability to measure tumor volume was evaluated with a measurement variability study, and by comparing in vivo and histologically measured tumor volume.

RESULTS

Eight hours after contrast injection the liver parenchyma and vasculature were equally enhanced allowing for clear delineation of the unenhanced tumors. The smallest tumor detected in this study was 1.1 mm in diameter. The coefficient of variation for tumor-volume measurement ranged from 3.6% to 12.9% and from 6.3% to 25.8% for intra and interobserver variability, respectively. In vivo and histologic tumor-volume measurements were closely correlated (r = 0.98, P < 0.0001).

CONCLUSIONS

Imaging at a time period of equal liver parenchyma and vascular enhancement after contrast injection allows for clear delineation of liver-tumor borders, thereby enabling quantitative tumor-volume monitoring.

Design-based stereological estimation of hepatocyte number, by combining the smooth optical fractionator and immunocytochemistry with anti-carcinoembryonic antigen polyclonal antibodies

BACKGROUND/AIMS

Hepatocytes (HEP) have been the major target for structural quantification in the liver, but an estimation of their total number (N), their percentage in relation to the global number of liver cells and the evaluation of the percentage of binucleated hepatocytes (BnHEPs) have never been performed with modern design-based stereological techniques. The establishment of sound technical guidelines and baseline quantitative data in non-pathological conditions are relevant to properly evaluate HEP hyperplasia and BnHEP responses.

METHODS

In this study, we combined immunocytochemistry with sound design-based stereology for estimating the N of HEP and the N of non-hepatocytic cells (NHCs). For obtaining systematic uniform random sections (30 microm thick), a smooth fractionator sampling scheme was applied to the liver of five male Wistar rats (3 month old). Those sections were immunostained with polyclonal antibodies against carcinoembryonic antigen. Because biliary canaliculi were then marked, an unequivocal counting of mononucleated hepatocytes (MnHEP) and BnHEP was allowed.

RESULTS

The N of HEP was estimated to be 1.93 x 10(9), with a coefficient of error (CE) of 0.02, corresponding to 129 x 10(6) HEP/g of liver. BnHEP represented 26% of total HEP number. The N of NHC was estimated as 1.31 x 10(9) (CE=0.02).

CONCLUSION

The strategy here presented provides a reliable method for accessing the N of HEP (distinguishing MnHEP from BnHEP) in situations in which these parameters are relevant, namely for evaluating the magnitude of an hyperplastic liver response from its very early onset.

Incorporating observability thresholds of tumors into the two-stage carcinogenesis model

This paper discusses a general way of incorporating the growth kinetics of malignant tumors with the two-stage carcinogenesis model. The model is presented using time-homogeneous rate parameters. In that case, the differential equations comprising the model are straightforward to solve using standard numerical techniques and software. An extension of the method to time-dependent rate parameters is included in Appendix A. Allowing the rate parameters to be time-dependent does incur computational cost. An expression is given for the expected time without visible tumor, a generalization of the expected time to an observable tumor that includes the possibility of tumor regression. The model is illustrated using incidental liver tumor data in control rats from NTP rodent carcinogenicity studies, using linear birth-death kinetics of tumors combined with a non-absorbing detection limit. The approach is also shown to be potentially useful with tumor observability thresholds having more complicated features.