Spectral Imaging Nov04 - caliperls.com file Spectral Imaging for Separation of Fluorescent Signals...
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www.cri-inc.com Spectral Imaging for Separation of Fluorescent Signals from Autofluorescence CRi, Inc. November 2004 SUMMARY: The ability to image and quantitate fluorescently labeled markers in vivo has generally been limited by the autofluorescence of the tissue. Skin, in particular, has a strong autofluorescence signal, particularly when excited in the blue or green wavelengths. Fluorescence labels with emission wavelengths in the near-infrared are more amenable to deep-tissue imaging because both scattering and autofluorescence are reduced as wavelengths are increased. However, autofluorescence still limits sensitivity. CRI has demonstrated that the signal obscuration caused by autofluorescence can be substantially eliminated using multispectral imaging techniques. INTRODUCTION: Autofluorescence, also known as background or tissue fluorescence, often poses a significant problem in many applications of fluorescence microscopy and small animal imaging. It contributes to unwanted noise, decreases contrast and can overwhelm the desired signal. Particularly difficult samples to image include many pathology specimens that have been processed using cross-linking fixatives (typically formaldehyde). This procedure dramatically increases the autofluorescence level, leading to bright, broad-spectrum emissions, particularly from connective tissue components. Unprocessed plant tissue and neuronal tissue also have extremely high levels of endogenous autofluorescence that can make many convenient labeling strategies, including most green fluorescent protein (GFP) labels, extremely problematic. Small animal imaging encounters autofluorescence primarily from components in skin (collagen, which fluoresces green) and food (chlorophyll, which fluoresces red). These can prevent detection of low-intensity signals in the visible range. PREVIOUS SOLUTIONS: Various solutions have been proposed for the reduction or elimination of autofluorescence. These include using narrow bandpass emission filters in an effort to isolate the desired fluorescence signal, the use of labels which can be excited at wavelengths that are much less likely to induce autofluorescence (moving the excitation towards the NIR is effective), and post-processing aldehyde-fixed samples with such reagents as sodium borohydride or toluidine blue to chemically suppress the autofluorescence signal. However, in many cases, these approaches are either infeasible or ineffective, and none of them fully eliminates the problem. SPECTRAL IMAGING: Spectral imaging delivers a high-resolution optical spectrum at every pixel of an image. This capability provides useful information beyond that which can be captured using the usual approaches utilizing color cameras, or monochrome cameras combined with conventional interference filters. CRI acquires spectral datasets by imaging a sample through a liquid crystal tunable filter (LCTF) that can be set to allow only light of a narrow bandpass (plus or minus 10 to 20 nm) to reach the camera; the peak position of this bandpass can be rapidly switched to any other position within milliseconds with about 1-nm precision. A series of images (typically 10 to 20) of a particular field can thus be rapidly acquired at different wavelengths to create a spectral data “cube”, in which the 3 dimensions are x, y and wavelength. In this cube, a spectrum is associated with every pixel. The resulting data can be used to identify, separate and remove the contribution of autofluorescence in analyzed images; the entire process can be completed in a matter of seconds. Spectral Imaging
Transcript of Spectral Imaging Nov04 - caliperls.com file Spectral Imaging for Separation of Fluorescent Signals...
www.cri-inc.com
Spectral Imaging for Separation of Fluorescent Signals fromAutofluorescence
CRi, Inc. November 2004
SUMMARY: The ability to image and quantitate fluorescently labeled markers in vivo has generally beenlimited by the autofluorescence of the tissue. Skin, in particular, has a strong autofluorescence signal,particularly when excited in the blue or green wavelengths. Fluorescence labels with emission wavelengthsin the near-infrared are more amenable to deep-tissue imaging because both scattering andautofluorescence are reduced as wavelengths are increased. However, autofluorescence still limitssensitivity. CRI has demonstrated that the signal obscuration caused by autofluorescence can besubstantially eliminated using multispectral imaging techniques.
INTRODUCTION: Autofluorescence, also known as background or tissue fluorescence, often poses a significantproblem in many applications of fluorescence microscopy and small animal imaging. It contributes to unwantednoise, decreases contrast and can overwhelm the desired signal. Particularly difficult samples to image includemany pathology specimens that have been processed using cross-linking fixatives (typically formaldehyde). Thisprocedure dramatically increases the autofluorescence level, leading to bright, broad-spectrum emissions, particularlyfrom connective tissue components. Unprocessed plant tissue and neuronal tissue also have extremely highlevels of endogenous autofluorescence that can make many convenient labeling strategies, including most greenfluorescent protein (GFP) labels, extremely problematic. Small animal imaging encounters autofluorescence primarilyfrom components in skin (collagen, which fluoresces green) and food (chlorophyll, which fluoresces red). Thesecan prevent detection of low-intensity signals in the visible range.
PREVIOUS SOLUTIONS: Various solutions have been proposed for the reduction or elimination of autofluorescence.These include using narrow bandpass emission filters in an effort to isolate the desired fluorescence signal, theuse of labels which can be excited at wavelengths that are much less likely to induce autofluorescence (movingthe excitation towards the NIR is effective), and post-processing aldehyde-fixed samples with such reagents assodium borohydride or toluidine blue to chemically suppress the autofluorescence signal. However, in many cases,these approaches are either infeasible or ineffective, and none of them fully eliminates the problem.
SPECTRAL IMAGING: Spectral imaging delivers a high-resolution optical spectrum at every pixel of an image.This capability provides useful information beyond that which can be captured using the usual approaches utilizingcolor cameras, or monochrome cameras combined with conventionalinterference filters. CRI acquires spectral datasets by imaging a sample througha liquid crystal tunable filter (LCTF) that can be set to allow only light of anarrow bandpass (plus or minus 10 to 20 nm) to reach the camera; the peakposition of this bandpass can be rapidly switched to any other position withinmilliseconds with about 1-nm precision. A series of images (typically 10 to 20)of a particular field can thus be rapidly acquired at different wavelengths tocreate a spectral data “cube”, in which the 3 dimensions are x, y andwavelength. In this cube, a spectrum is associated with every pixel. Theresulting data can be used to identify, separate and remove the contribution
of autofluorescence in analyzed images; the entire processcan be completed in a matter of seconds.
Spectral Imaging
CRI, Inc. 35 B Cabot Road Woburn, MA 01801 Tel: 781.935.9099 Fax: 781.935.3388 email: [email protected] website: www.cri-inc.com
Spectral Imaging for Separation of Fluorescent Signalsfrom Autofluorescence
SPECTRAL UNMIXING: Since fluorescent light emissions combine linearly (see figure), mixtures of signals canbe mathematically disentangled (or “unmixed”), as long as the spectrum of the desired signal(s) and that of theautofluorescence are known or can be deduced from the data, to yield images reflecting the abundance of eachcomponents isolated from the others. Since autofluorescence is “just” another spectral signal, it can be unmixedaway from exogenous signals.
EXAMPLE 1. Rare cells expressing low levels of YFP (yellowfluorescent protein) were present, but they could not bedifferentiated from unlabeled cells due to overlappingautofluorescence signals that were brighter than the YFPemission alone. After acquiring a spectral data set, spectracorresponding to autofluorescence and to YFP were usedin an unmixing algorithm that was able to separate the twosignals quantitatively. The single YFP-expressing cell in thefield of four cells shown on the right could be clearly seen.As this example demonstrates, even spectra with substantialoverlap but with different peak locations or shoulder-shapecan be separated quantitatively
EXAMPLE 2: A tumor in a mouse was detected usingIV-injection of antibodies targeting the tumor that werelabeled with fluorescent quantum dots emitting at 640nm. The top left image shows two mice (left, tumorbearing; right, control) after the injection. Thefluorescent signal from the quantum dots is hard todiscern against the large autofluorescent background.Using spectral imaging and unmixing, the tumor canbe clearly seen against a black background. The rightimage is composite of the tumor signal superimposedon the autofluorescence image.
Details of the algorithmic approaches, and manual andsemi-automated means for determining the spectraused in the unmixing procedure are described in aseparate application note.
References:
1. K.D. Niswender et al. Journal Of Microscopy, 180( Pt 2) (1995)109-162. R.M. Levenson, and D.L Farkas. Proc. SPIE, 2983(1997)1233. R.M. Levenson et al. Proc. SPIE 3438(1998)3004. Shuming Nie, Levenson, Nature Biotechnology, April, 2004.
Separation of “real” signal from similarautofluorescence signal in fluorescencemicroscopy
One cell is transfected with YFP (yellowfluorescent protein). The rest glow only withautofluorescence
Spectra of YFP(green) vs.autofluorescence(magenta)
Autofluorescence signal alone--after spectral unmixing
YFP signal alone-- after spectralunmixing
Tumor Signal
RGB Image
Quantum-dot labeledtumor deposits
Sample courtesy Shuming Nie, Emory University
