Analytical Strategy for Biopharmaceutical Development

Keyword(s):  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexander J. Dwyer ◽  
Jacob M. Ritz ◽  
Jason S. Mitchell ◽  
Tijana Martinov ◽  
Mohannad Alkhatib ◽  
...  

AbstractThe notion that T cell insulitis increases as type 1 diabetes (T1D) develops is unsurprising, however, the quantitative analysis of CD4+ and CD8+ T cells within the islet mass is complex and limited with standard approaches. Optical microscopy is an important and widely used method to evaluate immune cell infiltration into pancreatic islets of Langerhans for the study of disease progression or therapeutic efficacy in murine T1D. However, the accuracy of this approach is often limited by subjective and potentially biased qualitative assessment of immune cell subsets. In addition, attempts at quantitative measurements require significant time for manual analysis and often involve sophisticated and expensive imaging software. In this study, we developed and illustrate here a streamlined analytical strategy for the rapid, automated and unbiased investigation of islet area and immune cell infiltration within (insulitis) and around (peri-insulitis) pancreatic islets. To this end, we demonstrate swift and accurate detection of islet borders by modeling cross-sectional islet areas with convex polygons (convex hulls) surrounding islet-associated insulin-producing β cell and glucagon-producing α cell fluorescent signals. To accomplish this, we used a macro produced with the freeware software ImageJ equipped with the Fiji Is Just ImageJ (FIJI) image processing package. Our image analysis procedure allows for direct quantification and statistical determination of islet area and infiltration in a reproducible manner, with location-specific data that more accurately reflect islet areas as insulitis proceeds throughout T1D. Using this approach, we quantified the islet area infiltrated with CD4+ and CD8+ T cells allowing statistical comparison between different age groups of non-obese diabetic (NOD) mice progressing towards T1D. We found significantly more CD4+ and CD8+ T cells infiltrating the convex hull-defined islet mass of 13-week-old non-diabetic and 17-week-old diabetic NOD mice compared to 4-week-old NOD mice. We also determined a significant and measurable loss of islet mass in mice that developed T1D. This approach will be helpful for the location-dependent quantitative calculation of islet mass and cellular infiltration during T1D pathogenesis and can be combined with other markers of inflammation or activation in future studies.


2021 ◽  
pp. 000370282110245
Author(s):  
Qian Zhang ◽  
Minlu Ye ◽  
Lingyan Wang ◽  
Dongmei Jiang ◽  
Shuting Yao ◽  
...  

Multidrug resistance (MDR) is highly associated with poor prognosis of chronic myeloid leukemia (CML). This work aims to explore whether the laser tweezers Raman spectroscopy (LTRS) could be practical in separating adriamycin (ADR) resistance CML cells K562/ADR from its parental cells K562, and to explore the potential mechanisms. Detection of LTRS initially reflected the spectral differences caused by chemoresistance including bands assigned to carbohydrates, amino acid, protein, lipids and nucleic acid. In addition, principal components analysis (PCA) as well as the classification and regression trees (CRT) algorithms showed that the specificity and sensitivity were above 90%. Moreover, the band data-based CRT model and receiver operating characteristic (ROC) curve further determined some important bands and band intensity ratios to be reliable indexes in discriminating K562 chemoresistance status. Finally, we highlighted three metabolism pathways correlated with chemoresistance. This work demonstrates that the label-free LTRS analysis combined with multivariate statistical analyses have great potential to be a novel analytical strategy at the single-cell level for rapid evaluation the chemoresistance status of K562 cells.


Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3184
Author(s):  
Zhiyang Wu ◽  
Patrick Hundsdoerfer ◽  
Johannes H. Schulte ◽  
Kathy Astrahantseff ◽  
Senguel Boral ◽  
...  

Risk classification plays a crucial role in clinical management and therapy decisions in children with neuroblastoma. Risk assessment is currently based on patient criteria and molecular factors in single tumor biopsies at diagnosis. Growing evidence of extensive neuroblastoma intratumor heterogeneity drives the need for novel diagnostics to assess molecular profiles more comprehensively in spatial resolution to better predict risk for tumor progression and therapy resistance. We present a pilot study investigating the feasibility and potential of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to identify spatial peptide heterogeneity in neuroblastoma tissues of divergent current risk classification: high versus low/intermediate risk. Univariate (receiver operating characteristic analysis) and multivariate (segmentation, principal component analysis) statistical strategies identified spatially discriminative risk-associated MALDI-based peptide signatures. The AHNAK nucleoprotein and collapsin response mediator protein 1 (CRMP1) were identified as proteins associated with these peptide signatures, and their differential expression in the neuroblastomas of divergent risk was immunohistochemically validated. This proof-of-concept study demonstrates that MALDI-MSI combined with univariate and multivariate analysis strategies can identify spatially discriminative risk-associated peptide signatures in neuroblastoma tissues. These results suggest a promising new analytical strategy improving risk classification and providing new biological insights into neuroblastoma intratumor heterogeneity.


2021 ◽  
pp. 462102
Author(s):  
Adal Mena-García ◽  
Ana Isabel Ruiz-Matute ◽  
Ana Cristina Soria ◽  
María Luz Sanz

2020 ◽  
Vol 151 (2) ◽  
pp. 547-574 ◽  
Author(s):  
Lukas Salecker ◽  
Anar K. Ahmadov ◽  
Leyla Karimli

AbstractDespite significant progress in poverty measurement, few studies have undertaken an in-depth comparison of monetary and multidimensional measures in the context of low-income countries and fewer still in Sub-Saharan Africa. Yet the differences can be particularly consequential in these settings. We address this gap by applying a distinct analytical strategy to the case of Rwanda. Using data from two waves of the Rwandan Integrated Household Living Conditions Survey, we combine comparing poverty rates cross-sectionally and over time, examining the overlaps and differences in the two measures, investigating poverty rates within population sub-groups, and estimating several statistical models to assess the differences between the two measures in identifying poverty risk factors. We find that using a monetary measure alone does not capture high incidence of multidimensional poverty in both waves, that it is possible to be multidimensional poor without being monetary poor, and that using a monetary measure alone overlooks significant change in multidimensional poverty over time. The two measures also differ in which poverty risk factors they put emphasis on. Relying only on monetary measures in low-income sub-Saharan Africa can send inaccurate signals to policymakers regarding the optimal design of social policies as well as monitoring their effectiveness.


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