A framework for Cytome exploration
gregor_o at NOSPAMyahoo.com
Tue Jan 18 19:53:59 EST 2005
May I inquire if it might be possible to summarize your questions with a
slightly shorter expose? I am not familiar with the concept of reviewing
entire articles posted as a single newsgroup message. Especially the
paragraph about "Copyright notice and disclaimer" appears a little long. No?
Maybe I am just missing the point of your inquiry.
"Peter Van Osta" <pvosta_NO_SPAM_ at _NO_SPAM_cs.com> wrote in message
news:pan.2005.01.18.15.44.33.226234 at _NO_SPAM_cs.com...
> Besides my on-line version of my article on the Human Cytome Project and
> the application of cytomics in medicine and drug discovery I now start an
> article on a concept for large scale cytome exploration. It is at this
> moment just the beginning
> http://ourworld.compuserve.com/homepages/pvosta/hcpframe.htm and
> A framework for cytome exploration
> By Peter Van Osta
> Here I want to present and discuss some ideas on the exploration of the
> cytome and the conversion of the spatial, spectral and temporal properties
> of the cytome and its cells into their in-silico digital representation.
> We want to go from physics to quantitative features and finally come to an
> interpretation and understanding of the underlying biological process. We
> want to extract attributes from the physical process which are giving us
> information about the status and development of the process and its
> underlying structures.
> First we have to create an in-silico digital representation starting from
> the analogue reality captured by an instrument. The second stage (after
> creation of an in-silico representation) is to extract meaningful parts
> (objects) related to biologically relevant structures and processes.
> Thirdly we apply features to the extracted objects, such as area and
> (spectral) intensity, which represent (relevant) attributes of the
> observed structure and process. Finally we have to separate and cluster
> objects based on their feature properties into biologically relevant
> subgroups, such as healthy versus disease.
> In order to quantify the physical properties of space and time of a
> biological sample we must be able to create an appropriate digital
> representation of these physical properties in-silico. This digital
> representation is then accessible to algorithms for content extraction.
> The content or objects of interest are then to be presented to a
> quantification engine which associates physical meaningful properties or
> features to the extracted objects. These object features build a
> multidimensional feature space which can be inserted into feature
> analysers to find object/feature clusters, trends, associations and
> correlations. Managing the flow
> My personal interest is to build a framework in which acquisition,
> detection and quantification are designed as modules each using plug-ins
> to do the actual work and which operate on objects being transferred
> through the framework. Data representing space, time and spectral sampling
> are distributed throughout a data management system to be processed. The
> focus is not on the individual device to create the data or on individual
> algorithms, but on the management of the dataflow through a distributed
> system to convert spatial, spectral and temporal data into a feature
> (hyper-) space for quantitative analysis. A software framework manages the
> flow and transformation of data from physics to features. Up- and
> downscaling of cell-based research is dynamically managed by the system as
> the scale of processing does not require a change in basic design. I will
> mostly focus on imaging technology, but the basic principles should be
> applicable on any digitized content extraction process. Images are digital
> information matrixes of a higher order; they only become images as such
> when we want to look at them. Probing the sample
> When applying digital imaging technology to a biological sample, a clear
> understanding of the physical characteristics of the sample and its
> interaction with the â?osamplingâ? device is a prerequisite for a
> successful application of technology.
> The basic principle of a digital imaging system is to create a digital
> in-silico representation of the spatial, temporal and spectral physical
> process which is being studied. In order to achieve this we try to let
> down an equidistant sampling grid on the biological specimen. The physical
> layout of this sampling grid in reality is never a precise isomorphic
> cubical sampling pattern. The temporal and spectral sampling inner and
> outer resolution is determined by the physical characteristics of the
> sample (electromagnetic spectral range and spectral sampling layout) and
> the interaction with the detection technology being used.
> The instrument which converts the spatial (scale, dimensions), spectral
> (electromagnetic energy, wavelength) and temporal continuum of the sample
> into its digital representation allows us to take a view on biology beyond
> the capacity of our own perceptive system. It rescales space, spectrum and
> time into a digital representation accessible to human perception
> (contrast-range, colour) and ideally also to quantification. Instruments
> rescale spatial dimensions, spectral ranges and time into a scale which is
> accessible to the human mind. The digital image acts as a see-through
> window on a part of the physical properties of the biological sample, not
> on the instrument as such.
> We want to insert a probe system into the sample which changes its state
> according to the physical characteristics of the sample. The changes in
> the probe system are ideally perfectly aligned in a spatial-spectral and
> temporal space with the physical properties of the sample itself. Each
> probe system senses the state of the specimen with a finite aperture and
> so provides us with a view on the biological structure. As such all
> sensing is done in XYZ, spectrum and time, it is the inner an outer
> resolution of our sampling which changes. When we do 2D imaging, this the
> same as 3D with the 3rd dimension collapsed to one layer, but due to the
> Depth of Focus (D.O.F.) this represents a physical Z-slice.
> In the spectral domain we probe electromagnetic energy along the spectral
> axis with a certain inner and outer resolution. We slide up and down the
> spectral axis within the limits of one spectral probing system, which
> transforms electromagnetic energy. A single CCD camera probes the visible
> spectrum in one sweep. A 3CCD camera uses 3 probes to do its spectral
> sampling. However increasing or decreasing the density of the spectral
> sampling is only a matter of spectral dynamics. We tend to use â?ospectra
> imagingâ? for anything which samples the visible spectrum with more than
> the spectral resolution of a 3CCD camera. Up-and downscaling our spectral
> sampling from broad to narrow, parallel or sequential, continuous or
> discontinuous is a matter of applying an appropriate detector array. A
> system can manage 1 to n spectral probing devices such as cameras or
> PMTâ?Ts each sampling a part of the spectrum and spatially aligned allows
> to probe the spectrum in a dynamic way.
> The time axis is also probed with a varying temporal inner and outer
> resolution and depending on the characteristics of the detection device;
> the time-slicing can be collapsed or expanded. Time can be sampled
> continuously or discontinuously (time-lapse).
> The result is a 5-dimensional system expanding or collapsing each
> dimension (XYZ, lambda, time) according to the requirements of
> exploration. The device attached to the exploration core, imposes the
> inner and outer resolution limits upon the system. In-silico these are
> only high-order matrix arrays representing a 5D space. We could call this
> a continuously variable in-silico representation.
> The inner an outer resolution of the probing system is determined by the
> physical XYZ sampling characteristics of the sampling device, such as its
> point spread function (PSF). For a digital microscope the resolving power
> of the objective (XYZ) and its depth of view/focus are important issues in
> experimental design and determining the application range of a device. The
> interaction of the detection device with the image created by the optics
> of the system such as Nyquist sampling demands, distribution of spectral
> sensitivity, dynamic range, also plays an important role. The pixel or
> voxel representation in-silico however is basically â?ounawareâ? of this
> meta-information about how the digital density pattern was created.
> Detection and quantification algorithms act on the digital information as
> such and only the back-translation into physical meaningful data requires
> a back-propagation into the real-world layout and dimensions.
> How do we physically organize the sampling of biological specimen? The
> exploration of cellular or tissue samples is organised in an
> array-pattern, ranging form a single tissue slice on a glass slide up to a
> large scale grid of for instance a cell or tissue expression arrays. The
> granularity or density of the array pattern is determined by the
> experimental demands and upstream and downstream processing capacity. Of
> course the optical characteristics of the sample carrier (glass, plastic)
> will determine the spatial sampling limits in its inner and outer
> resolution. The optical and mechanical characteristics of the device used
> to explore the (sub) cellular physical domain will also lead to a spatial,
> spectral and temporal application domain. The coarse grid-like pattern of
> samples on a sample carrier is being explored at each array position at
> the appropriate inner and outer resolution, within the optical physical
> boundaries of the device used to capture the data. The outer resolution
> barrier of the individual detector in space and time is extended by both
> spatial and temporal tiling at a range of intervals. Spectral multiplexing
> is being done by using spectral selection devices with the appropriate
> spectral characteristics for the spectral profile of the sample.
> The resulting discrete representation of the sampled spatial, spectral and
> temporal grid at each array position is being sent to a storage medium to
> provide an audit trail for quality assessment and data validation.
> The detection of appropriate objects for further quantification is done
> either in-line within the acquisition process or distributed to another
> process dealing with the object selection.
> The selected objects are sent to a quantification module which attaches an
> array of quantitative descriptors (shape, density â?¦) to each object.
> Objects belonging to the same biological entity are tagged to allow for a
> linked exploration of the feature space created for each individual
> object. The resulting data arrays can be fed into analytical tools
> appropriate for analysing a high dimensional linked feature space or
> feature hyperspace.
> Copyright notice and disclaimer
> My web pages represent my interests, my opinions and my ideas, not those
> of my employer or anyone else. I have created these web pages without any
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> information here is provided in good faith but no warranty can be made for
> its accuracy. As this is a work in progress, it is still incomplete and
> even inaccurate. Although care has been taken in preparing the information
> contained in my web pages, I do not and cannot guarantee the accuracy
> thereof. Anyone using the information does so at their own risk and shall
> be deemed to indemnify me from any and all injury or damage arising from
> such use. To the best of my knowledge, all graphics, text and other
> presentations not created by me on my web pages are in the public domain
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> have any questions, send me an email.
> First on-line version published on 9 Jan. 2005, last update on 10 Jan.
> Email: pvosta_NOJUNK_ at _NOJUNK_cs.com remove the _NOJUNK_ before sending
> an email.
> The author of this webpage is Peter Van Osta, MD.
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