Jari Niemi

Global Gene Expression Profile of Human Cord Blood–Derived CD133+ Cells

Human cord blood (CB)–derived CD133+ cells carry characteristics of primitive hematopoietic cells and proffer an alternative for CD34+ cells in hematopoietic stem cell (HSC) transplantation. To characterize the CD133+ cell population on a genetic level, a global expression analysis of CD133+ cells was performed using oligonucleotide microarrays. CD133+ cells were purified from four fresh CB units by immunomagnetic selection. All four CD133+ samples showed significant similarity in their gene expression pattern, whereas they differed clearly from the CD133+ control samples. In all, 690 transcripts were differentially expressed between CD133+ and CD133+ cells. Of these, 393 were increased and 297 were decreased in CD133+ cells. The highest overexpression was noted in genes associated with metabolism, cellular physiological processes, cell communication, and development. A set of 257 transcripts expressed solely in the CD133+ cell population was identified. Colony‐forming unit (CFU) assay was used to detect the clonal progeny of precursors present in the studied cell populations. The results demonstrate that CD133+ cells express primitive markers and possess clonogenic progenitor capacity. This study provides a gene expression profile for human CD133+ cells. It presents a set of genes that may be used to unravel the properties of the CD133+ cell population, assumed to be highly enriched in HSCs.

Estimation and inversion of the effects of cell population asynchrony in gene expression time-series

We introduce several approaches to improve the quality of gene expression data obtained from time-series measurements by applying signal processing tools. Performance of the proposed methods are examined using both simulated and real yeast gene expression data. In particular, we concentrate especially on a smoothing effect caused by the distribution of the cell population in time and introduce several methods for inverting this phenomenon. The proposed methods can be used to significantly improve the accuracy of the gene expression time-series measurements since the cell population asynchrony (wide distribution) is inevitably caused by the different operation pace of the cells. Some of the proposed methods rely on the partition of the genes, as well as the corresponding expression profiles, into the cell cycle regulated and noncell cycle regulated genes. For that purpose, we first study the cell cycle regulated genes and introduce a method that can be used to estimate the period length of those genes. We also estimate the spreading rate of the underlying distribution of the cell population based solely on the observed gene expression data. After the preliminary experiments, we introduce some methods for estimating the underlying distribution of the cell population instead of its spreading rate. These methods assume certain additional measurements, such as flow cytometry (e.g. fluorescent-activated cell sorter (FACS)) or bud counting measurements, to be available. We also apply the standard blind deconvolution method for estimating the true distribution of the cell population. The found estimates of the spreading rate of the cell distribution and the distributions of the cell population themself are used to invert the smoothing effect. To that end, we discuss some inversion approaches applicable to the problem in hand.

Functional Brain Segmentation Using Inter-Subject Correlation in fMRI

The human brain continuously processes massive amounts of rich sensory information. To better understand such highly complex brain processes, modern neuroimaging studies are increasingly utilizing experimental setups that better mimic daily‐life situations. A new exploratory data‐analysis approach, functional segmentation inter‐subject correlation analysis (FuSeISC), was proposed to facilitate the analysis of functional magnetic resonance (fMRI) data sets collected in these experiments. The method provides a new type of functional segmentation of brain areas, not only characterizing areas that display similar processing across subjects but also areas in which processing across subjects is highly variable. FuSeISC was tested using fMRI data sets collected during traditional block‐design stimuli (37 subjects) as well as naturalistic auditory narratives (19 subjects). The method identified spatially local and/or bilaterally symmetric clusters in several cortical areas, many of which are known to be processing the types of stimuli used in the experiments. The method is not only useful for spatial exploration of large fMRI data sets obtained using naturalistic stimuli, but also has other potential applications, such as generation of a functional brain atlases including both lower‐ and higher‐order processing areas. Finally, as a part of FuSeISC, a criterion‐based sparsification of the shared nearest‐neighbor graph was proposed for detecting clusters in noisy data. In the tests with synthetic data, this technique was superior to well‐known clustering methods, such as Wards method, affinity propagation, and K‐means ++ . Hum Brain Mapp 38:2643–2665, 2017.

Error-corrected estimation of regional kinetic parameter histograms directly from pet projections

Positron emission tomography (PET) is a unique method to investigate physiology in the living body. Kinetic models with kinetic rate constants describe the dynamic radioactive tracer uptake in living tissue. If the variation of the kinetic parameter values within a specific tissue region could be determined accurately, it would give valuable quantitative information about the tissue heterogeneity. In this study we developed a unique method to estimate the variation from the regional kinetic parameter histograms. To determine the kinetic parameter values, we chose non-penalized maximum likelihood (ML) estimation due to the specific statistical error properties of the ML estimates. The parameter values were estimated directly from the time series of PET projections. The choice of the estimation method enabled us to utilize the ML theory in error correction. We developed a Monte Carlo approach to determine the regional error distributions. The true variation of the kinetic parameters could then be revealed by correcting the regional ML estimate histograms with the estimated error distributions. The method was tested with simulated data. In simulations both the average and the deviation of the kinetic parameters were determined from the error-corrected histograms with good numerical accuracy for the selected region of interest.

ShakeMe: Key Generation from Shared Motion

Devices equipped with accelerometer sensors such as todays mobile devices can make use of motion to exchange information. A typical example for shared motion is shaking of two devices which are held together in one hand. Deriving a shared secret (key) from shared motion, e.g. for device pairing, is an obvious application for this. Only the keys need to be exchanged between the peers and neither the motion data nor the features extracted from it. This makes the pairing fast and easy. For this, each device generates an information signal (key) independently of each other and, in order to pair, they should be identical. The key is essentially derived by quantizing certain well discriminative features extracted from the accelerometer data after an implicit synchronization. In this paper, we aim at finding a small set of effective features which enable a significantly simpler quantization procedure than the prior art. Our tentative results with authentic accelerometer data show that this is possible with a competent accuracy (76%) and key strength (entropy approximately 15 bits).

RMBNToolbox: random models for biochemical networks

BackgroundThere is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models.ResultsWe present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language.ConclusionWhile more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.

Regional compensation for statistical maximum likelihood reconstruction error of PET image pixels.

In positron emission tomography (PET), there is an increasing interest in studying not only the regional mean tracer concentration, but its variation arising from local differences in physiology, the tissue heterogeneity. However, in reconstructed images this physiological variation is shadowed by a large reconstruction error, which is caused by noisy data and the inversion of tomographic problem. We present a new procedure which can quantify the error variation in regional reconstructed values for given PET measurement, and reveal the remaining tissue heterogeneity. The error quantification is made by creating and reconstructing the noise realizations of virtual sinograms, which are statistically similar with the measured sinogram. Tests with physical phantom data show that the characterization of error variation and the true heterogeneity are possible, despite the existing model error when real measurement is considered.

An image reconstruction that preserves the regional physiological variation of radioactivity concentration

Regularization of PET reconstruction is needed to suppress noise in images, but the level of smoothness is difficult to choose. The proper amount of image variation should be close to the one that arises from physiology, for example local differences in glucose uptake. However, this small natural variation is mixed with the large noise component. We present a method which estimates and cancels the amount of statistical reconstruction noise and reveals the true regional variation of PET image pixels. This information can then be used as a simple stopping rule for Maximum Likelihood reconstruction, resulting in image whose pixel values contain the true, natural amount of variation. The performance of the method was tested for phantoms with different ROI sizes and sinogram count levels. We demonstrated the working limits where the error is significant before the correction, and where the method is sufficiently robust.

Estimation of kinetic parameters and tissue heterogeneity from patient motion corrupted PET projections

Patient motion lowers the resolution of modern PET scanners and hinders quantitative analysis. Accurate determination of the variation of regional kinetic parameter values can be viewed as a metric for tissue heterogeneity and gives new quantitative information. We have included a rigid motion model into an existing method for estimating the voxel-wise kinetic parameters from sinograms. In this method, the motion parameters are estimated simultaneously together with the other model parameters. No pre- or post processing of the data is needed. We have tested our method on numerical simulations with two noise levels. For low noise level, the motion and voxel-wise kinetic parameters were estimated succesfully. For the relatively high noise level, the motion estimation was successful when regionally homogeneous tissue was assumed.

Direct estimation of regional kinetic rate constant distributions from PET sinograms and correction of statistical estimation error distributions

We present a novel method to estimate the variation of kinetic model parameters directly from sinogram time series data. We determine the kinetic parameters independently for each voxel of the target through mmriimim likelihood estimation. The maximum likelihood estimates in the region of interest are collected into histograms. The histograms contain estimation error, which obeys maximum likelihood theory. We determine the error distribution through repeated estimation with a simulated homogeneous target With simulated sinogram time series data we evaluate the methods performance with the 2K-model and the 3K-model. The results show that after error correction both the mean and the deviation can be obtained with good accuracy from the histograms.