MNE’s Regional Location Choice - A Comparative Perspective on East Germany, the Czech Republic and Poland

The focus of this article is the empirical identification of factors influencing Foreign Direct Investment (FDI) in transition economies on a regional level (NUTS 2). The analysis is designed as benchmark between three neighboring post-communist regions, i.e. East Germany, the Czech Republic and Poland. Their different transition paths have not only resulted in economic differences. We can also observe today that the importance of pull factors for FDI varies significantly across the regions. This analysis shows that in comparison with Poland and the Czech Republic, East Germany’s major benefit is its purchasing power, its geographical proximity to West European markets, and its modern infrastructure. Furthermore, the analysis suggests that intra-industry linkages such as specialization and agglomeration economies are relevant factors for the location decision of foreign investors. This result can help to explain the regional divergence of FDI streams in transition economies.multinational enterprises, international business, regional economic activity: growth, development, and changes, discrete choice

Subnanoliter enzymatic assays on microarrays

Many areas of research today are based on enzymatic assays most of which are still performed as enzyme-linked immunosorbent assays in microtiter plates. The demand for highly parallel screening of thousands of samples eventually led to a miniaturization and automation of these assays. However, the final transfer of enzymatic assays from a microtiter-based technology to microarrays has proven to be difficult for various reasons, such as the inability to maintain unbound reaction products on the spot of reaction or the missing capability of multiplexing. Here, we have conducted multiplex enzymatic assays in subnanoliter volumes on a single microarray using the multiple spotting technology. We were able to measure enzymatic activity with a sensitivity down to 35 enzyme molecules, applying only conventional flat microarray surfaces and standard microarray hardware. We have performed assays of inhibition and applied this format for the detection of prognostic markers, such as cathepsin D. The new approach allows the rapid and multiplex screening of thousands of samples on a single microarray with applications in drug screening, metagenomics, and high-throughput enzyme assays

Bacterial protein microarrays for identification of new potential diagnostic markers for Neisseria meningitidis infections

Neisseria meningitidis is the most common cause of meningitis and causes epidemic outbreaks. One trait of N. meningitidis, which is associated with most of the currently recognized virulence determinants, is the presence of phase-variable genes that are suspected to enhance its ability to cause an invasive disease. To detect the immune responses to phase-variable expressed proteins, we applied protein microarray technology for the screening of meningitis patient sera. We amplified all 102 known phase-variable genes from N. meningitidis serogroup B strain MC58 by polymerase chain reaction and subcloned them for expression in Escherichia coli. With this approach, we were able to express and purify 67 recombinant proteins representing 66% of the annotated genes. These were spotted robotically onto coated glass slides to generate protein microarrays, which were screened using 20 sera of patients suffering from meningitis, as well as healthy controls. From these screening experiments, 47 proteins emerged as immunogenic, exhibiting a variable degree of seroreactivity with some of the patient sera. Nine proteins elicited an immune response in more than three patients, with one of them, the phase-variable opacity protein OpaV (NMB0442), showing responses in 11 patient sera. This is the first time that protein microarray technology has been applied for the investigation of genetic phase variation in pathogens. The identification of disease-specific proteins is a significant target in biomedical research, as such proteins may have medical, diagnostic, and commercial potential as disease markers

Clonally resolved single-cell multi-omics identifies routes of cellular differentiation in acute myeloid leukemia

Inter-patient variability and the similarity of healthy and leukemic stem cells (LSCs) have impeded the characterization of LSCs in acute myeloid leukemia (AML) and their differentiation landscape. Here, we introduce CloneTracer, a novel method that adds clonal resolution to single-cell RNA-seq datasets. Applied to samples from 19 AML patients, CloneTracer revealed routes of leukemic differentiation. Although residual healthy and preleukemic cells dominated the dormant stem cell compartment, active LSCs resembled their healthy counterpart and retained erythroid capacity. By contrast, downstream myeloid progenitors constituted a highly aberrant, disease-defining compartment: their gene expression and differentiation state affected both the chemotherapy response and leukemia's ability to differentiate into transcriptomically normal monocytes. Finally, we demonstrated the potential of CloneTracer to identify surface markers misregulated specifically in leukemic cells. Taken together, CloneTracer reveals a differentiation landscape that mimics its healthy counterpart and may determine biology and therapy response in AML

Studies on the optimisation and application of protein arrays

The sequencing of the human genome and other ongoing sequencing projects have accelerated the pace of gene discovery and caused the identification of thousands of new genes. However, it also entails realisation that the genome alone could not provide enough information to understand the complex cellular network on the molecular level. Although genetic information provides us with the sequence of each protein, it is currently not possible to entirely deduce its localisation, structure, modifications, interactions, activities, and, ultimately, their function from it sequence. This lack of information becomes especially obvious upon observation of a relatively closely linked relationship, the stoichiometry between RNA transcripts and their corresponding protein abundances. Although gene-protein dynamics were analysed for several tissues (1, 2), there is still no reliable correlation between gene activity and protein abundance. Besides this, protein abundances and their entirety, the proteome, are highly dynamic and therefore require tools that are amenable for describing several variables simultaneously. Up to today two-dimensional (2D) gel electrophoresis for protein separation, followed by mass spectrometry (MS) and database searches for protein identification, are the only real high-throughput techniques for the complex description of a proteome. They are especially important in the classical proteome analysis, which focuses on studying complete proteomes, e.g. from two differentially treated cell lines, and the corresponding identification of single proteins