Stain deconvolution using statistical analysis of multi-resolution stain colour representation

Stain colour estimation is a prominent factor of the analysis pipeline in most of histology image processing algorithms. Providing a reliable and efficient stain colour deconvolution approach is fundamental for robust algorithm. In this paper, we propose a novel method for stain colour deconvolution of histology images. This approach statistically analyses the multi-resolutional representation of the image to separate the independent observations out of the correlated ones. We then estimate the stain mixing matrix using filtered uncorrelated data. We conducted an extensive set of experiments to compare the proposed method to the recent state of the art methods and demonstrate the robustness of this approach using three different datasets of scanned slides, prepared in different labs using different scanners

Simultaneous automatic scoring and co-registration of hormone receptors in tumour areas in whole slide images of breast cancer tissue slides

Aims: Automation of downstream analysis may offer many potential benefits to routine histopathology. One area of interest for automation is in the scoring of multiple immunohistochemical markers in order to predict the patient's response to targeted therapies. Automated serial slide analysis of this kind requires robust registration to identify common tissue regions across sections. We present an automated method for co-localised scoring of Estrogen Receptor and Progesterone Receptor (ER/PR) in breast cancer core biopsies using whole slide images. Methods and Results: Regions of tumour in a series of fifty consecutive breast core biopsies were identified by annotation on H&E whole slide images. Sequentially cut immunohistochemical stained sections were scored manually, before being digitally scanned and then exported into JPEG 2000 format. A two-stage registration process was performed to identify the annotated regions of interest in the immunohistochemistry sections, which were then scored using the Allred system. Overall correlation between manual and automated scoring for ER and PR was 0.944 and 0.883 respectively, with 90% of ER and 80% of PR scores within in one point or less of agreement. Conclusions: This proof of principle study indicates slide registration can be used as a basis for automation of the downstream analysis for clinically relevant biomarkers in the majority of cases. The approach is likely to be improved by implantation of safeguarding analysis steps post registration

Hyper-stain inspector : a framework for robust registration and localised co-expression analysis of multiple whole-slide images of serial histology sections

In this paper, we present a fast method for registration of multiple large, digitised whole-slide images (WSIs) of serial histology sections. Through cross-slide WSI registration, it becomes possible to select and analyse a common visual field across images of several serial section stained with different protein markers. It is, therefore, a critical first step for any downstream co-localised cross-slide analysis. The proposed registration method uses a two-stage approach, first estimating a fast initial alignment using the tissue sections’ external boundaries, followed by an efficient refinement process guided by key biological structures within the visual field. We show that this method is able to produce a high quality alignment in a variety of circumstances, and demonstrate that the refinement is able to quantitatively improve registration quality. In addition, we provide a case study that demonstrates how the proposed method for cross-slide WSI registration could be used as part of a specific co-expression analysis framework

5′ Unlocked Nucleic Acid Modification Improves siRNA Targeting

Optimization of small interfering RNAs (siRNAs) is important in RNA interference (RNAi)-based therapeutic development. Some specific chemical modifications can control which siRNA strand is selected by the RNA-induced silencing complex (RISC) for gene silencing. Intended strand selection will increase potency and reduce off-target effects from the unintended strand. Sometimes, blocking RISC loading of the unintended strand leads to improved intended strand-silencing potency, but the generality of this phenomenon is unclear. Specifically, unlocked nucleic acid (UNA) modification of the 5′ end of canonical (i.e., 19+2) siRNAs abrogates gene silencing of the modified strand, but the fate and potency of the unmodified strand has not been investigated. Here, we show that 5′ UNA-modified siRNAs show improved silencing potency of the unmodified strand. We harness this advantageous property in a therapeutic context, where a limited target region in a conserved HIV 5′ long terminal repeat U5 region would otherwise yield siRNAs with undesired strand selection properties and poor silencing. Applying 5′ UNA modification to the unintended sense (S) strand of these otherwise poorly targeted siRNAs dramatically improves on-target silencing by the intended antisense (AS) strand in pNL4-3.luciferase studies. This study highlights the utility of 5′ UNA siRNA modification in therapeutic contexts where siRNA sequence selection is constrained

Engineering RNA for targeted siRNA delivery and medical application

RNA engineering for nanotechnology and medical applications is an exciting emerging research field. RNA has intrinsically defined features on the nanometer scale and is a particularly interesting candidate for such applications due to its amazing diversity, flexibility and versatility in structure and function. Specifically, the current use of siRNA to silence target genes involved in disease has generated much excitement in the scientific community. The intrinsic ability to sequence-specifically down-regulate gene expression in a temporally- and spatially-controlled fashion has led to heightened interest and rapid development of siRNA-based therapeutics. Though methods for gene silencing with high efficacy and specificity have been achieved in vitro, the effective delivery of nucleic acids to specific cells in vivo has been a hurdle for RNA therapeutics. This review covers different RNA-based approaches for diagnosis, prevention and treatment of human disease, with a focus on the latest developments of nonviral carriers of siRNA for delivery in vivo. The applications and challenges of siRNA therapy, as well as potential solutions to these problems, the approaches for using phi29 pRNA-based vectors as polyvalent vehicles for specific delivery of siRNA, ribozymes, drugs or other therapeutic agents to specific cells for therapy will also be addressed

Correlation between the density maps and the ground truth with the associated <i>p-values</i> above each method for the H (left) and E (right) stains in all the three datasets.

<p>Indices a, b, c, d, and e of the x-axis show the correlation results of among all datasets for the Proposed method, Macenko <i>et al.</i> [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref011" target="_blank">11</a>], Ruifrok and Johnston [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref010" target="_blank">10</a>], BCD [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref013" target="_blank">13</a>],and ICA [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref012" target="_blank">12</a>], respectively Notice that most of the proposed methods perform similarly in estimating H satin (left). However, the weaker stain (E) is more challenging to estimate (right). Proposed method keeps its performance in estimating Eosin stain with mean significance of <i>p-value</i> < 0.05.</p

Correlation between the density maps and the ground truth.

<p>Indices a, b, c, d, and e of the x-axis show the correlation results for the Proposed method, Macenko <i>et al.</i> [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref011" target="_blank">11</a>], Ruifrok and Johnston [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref010" target="_blank">10</a>], BCD [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref013" target="_blank">13</a>],and ICA [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref012" target="_blank">12</a>], respectively. Due to the high difference in the correlation margin between ICA and the other algorithms in the H density estimation for the second dataset, ICA has been removed in order to make the correlations of the other algorithms noticeable.</p

Estimation of Eosin channel for a sample image.

<p>Images a,b,c,d and e corresponds to the original image, Ruifrok and Johnston [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref010" target="_blank">10</a>], Macenko <i>et al.</i> [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref011" target="_blank">11</a>], BCD [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref013" target="_blank">13</a>], respectively. We can notice in Ruifrok and Johnston method [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref010" target="_blank">10</a>] that the pre-estimated mixing parameters is actually not reflecting the Eosin stain colour distribution in the original image. In Macenko <i>et al.</i> [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref011" target="_blank">11</a>] method, the colour estimation is affected by the correlation between the two stain colours. In BCD method [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref013" target="_blank">13</a>], the fine variation within the H stain is merged with the E due to the projection on the chromaticity plane. In the proposed method however, the variation of the stain colour distribution in the original image is perfectly reflected and H channel is smoothly separated.</p

Block diagram of the proposed method.

<p>Block diagram of the proposed method.</p

Results of nuclei detection algorithm in [2, 28] trained and tested for different stain deconvolution algorithms.

<p>Values show the mean and standard deviation for each of the precision, recall, and F1 score measures. Note that the evaluated algorithms are dynamically estimating stain colour based on current information. Thus, consistency of the algorithm could improve the detection accuracy. However, we did not include stain normalization in this experiments to avoid affecting the deconvolution results.</p